AI in Ophthalmology | Startup Landscape

Artificial intelligence in ophthalmology
Business and startup landscape
Petteri Teikari, PhD
http://petteri-teikari.com/
version Mon 12 September 2016

Introduction
●
Shallow introduction for ophthalmologic / healthcare market in a dense
format
– Mainly meant for people with machine learning background with little
knowledge to the healthcare sector
– Best to be read from a tablet (or similar device with easy zoom in/out), do
not work very well for project despite the slide format.
●
Purpose of the presentation to illustrate the “non-technical” complexities
related to healthcare ventures which not be that obvious from start.
– Not sufficient necessarily to have technically sophisticated AI solutions if no
one is willing to pay for it, and you don't know understand how to bring
value to the clinicians.

What modality is the best for diagnosis?
From “old school methods” (visual field, fundus
photograph and SD-OCT), the SD-OCT seems
to offer clearly the best diagnostic capability
Results:” Among the four specialists, the interobserver agreement
across the three diagnostic tests was poor for VF and photos, with
kappa ( ) values of 0.13 and 0.16, respectively, and moderate for OCT,κ
with value of 0.40. Using panel consensus as reference standard, OCTκ
had the highest discriminative ability, with an area under the curve
(AUC) of 0.99 (95% 0.96–1.0) compared to photograph AUC 0.85 (95%
0.73–0.96) and VF AUC 0.86 (95% 0.76–0.96), suggestive of closer
performance to that of a group of glaucoma specialists.”
Blumberg et al. (2016)
Retinal Diseases Signs In One Picture
ophthnotes.com/retinal-diseases-signs-in-one-picture
+ Bone spicule pigments (BSP) in Retinitis pigmentosa (RP), Chorioretinal
Atrophy, Congenital hypertrophy of the retinal pigment epithelium (CHRPE),
Asteroid hyalosis, Haemangioma, Choroidal neovascularization (CNV),
Retinoschisis, etc.
For more
detailed
analysis see →

Future of OCT and retinal biomarkers
●
From Schmidt-Erfurth et al. (2016): “The therapeutic efficacy of VEGF inhibition in combination with the potential
of OCT-based quantitative biomarkers to guide individualized treatment may shift the medical need from CNV
treatment towards other and/or additional treatment modalities. Future therapeutic approaches will likely focus on
early and/or disease-modifying interventions aiming to protect the functional and structural integrity of the
morphologic complex that is primarily affected in AMD, i.e. the choriocapillary - RPE – photoreceptor unit.
Obviously, new biomarkers tailored towards early detection of the specific changes in this functional unit will be
required as well as follow-up features defining the optimal therapeutic goal during extended therapy, i.e. life-long in
neovascular AMD. Three novel additions to the OCT armamentarium are particularly promising in their
capability to identify the biomarkers of the future:”
Polarization-sensitive OCT OCT angiography Adaptiveopticsimaging
“this modality is particularly appropriate to highlight early
features during the pathophysiological development of
neovascular AMD
Findings from studies using adaptive optics implied that
decreased photoreceptor function in early AMD may be
possible, suggesting that eyes with pseudodrusen appearance may
experience decreased retinal (particularly scotopic) function in
AMD independent of CNV or RPE atrophy.”
“...the specific patterns of RPE plasticity
including RPE atrophy, hypertrophy, and
migration can be assessed and quantified).
Moreover, polarization-sensitiv e OCT allows
precise quantification of RPE-driven disease at
the early stage of drusen”,
“Angiographic OCT with its potential
to capture choriocapillary, RPE, and
neuroretinal fetures provides novel
types of biomarkers identifying disease
pathophysiology rather than late
consecutive features during advanced
neovascular AMD.””
Schlanitz et al. (2011)
zmpbmt.meduniwien.ac.at
See also Leitgeb et al. (2014)
Zayit-Soudry et al. (2013)

Future Multispectral Imaging
http://dx.doi.org/10.1038/eye.2011.202
Absorption spectra for the major absorbing elements of the eye. Note that some of the spectra change with
relatively small changes in wavelength. Maximizing the differential visibility requires utilizing small spectral
slices. Melanin is the dominant absorber beyond 600 nm.
Zimmer et al. (2014)
Zimmer et al. (2014)
The Annidis RHA™ system combines advanced multispectral imaging (MSI) technology with
multi-image software processing for early detection of ocular pathologies such as age
related macular degeneration, diabetic retinopathy and glaucoma.
http://www.annidis.com/page/technology

OCT towards handheld devices
http://dx.doi.org/10.1364/BOE.5.000293
Cited by 5410.1038/nphoton.2016.141
http://dx.doi.org/10.1364/OE.24.013365
Here, we report the design and operation of a handheld probe that can perform both scanning
laser ophthalmoscopy and optical coherence tomography of the parafoveal photoreceptor
structure in infants and children without the need for adaptive optics. The probe, featuring a
compact optical design weighing only 94 g, was able to quantify packing densities of
parafoveal cone photoreceptors and visualize cross-sectional photoreceptor substructure in
children with ages ranging from 14 months to 12 years.
https://aran.library.nuigalway.ie/handle/10379/5481

Fundus handheld
https://www.jedmed.com/products/hd-digital-scope-system
http://www.epipole.com/
http://dx.doi.org/10.1167%2Fiovs.12-10449, Cited by 22 articles
http://dx.doi.org/10.1089/tmj.2015.0068

OCT Example report from Zeiss Cirrus

OCT Device Comparison
Comparison of images obtained with 3 different
spectral-domain OCT devices (Topcon 3D OCT-
1000, Zeiss Cirrus, Heidelberg Spectralis) of both
eyes of the same patient with early AMD changes
taken just minutes apart.
Comparison of images obtained with 3 different
spectral-domain OCTs (Heidelberg Spectralis,
Optovue RTVue, Topcon 3D OCT-1000) and with 1
time-domain OCT (Zeiss Stratus) of both eyes of
the same patient with a history of central serous
chorioretinopathy in both eyes.
The same set of images as shown above in
pseudocolor.
Comparison of horizontal B-scan images and 3D
images of a patient with neovascular age-related
macular degeneration obtained with Heidelberg
Spectralis, Zeiss Cirrus, Topcon 3D OCT-1000.
Spectral-domain Optical
Coherence Tomography: A
Real-world Comparison
IRENE A. BARBAZETTO, MD · SANDRINE A. ZWEIFEL,
MD · MICHAEL ENGELBERT, MD, PhD · K. BAILEY
FREUND, MD · JASON S. SLAKTER, MD
retinalphysician.com
reviewofophthalmology.com

Typical volumetric medical formats
DICOM NIFTI .NII
brainder.org
http://nipy.org/nibabel/gettingstarted.html
http://people.cas.sc.edu/rorden/dicom/index.html
http://dicom.nema.org/
NEMA standard PS3, and as ISO standard 12052:2006
Practically outdated but still used
The Nifti format has rapidly replaced the Analyze in neuroimaging
research, being adopted as the default format by some of the most
widespread public domain software packages, as, FSL [12], SPM [13],
and AFNI [14]. The format is supported by many viewers and image
analysis software like 3D Slicer [15], ImageJ [16], and OsiriX, as well as
other emerging software like R [17] and Nibabel [18], besides various
conversion utilities.
An update version of the standard, the Nifti-2, developed to manage
larger data set has been defined in the 2011. This new version encode
each of the dimensions of an image matrix with a 64-bit integer instead
of a 16-bit as in the Nifti-1, eliminating the restriction of having a size
limit of 32,767. This updated version maintains almost all the
characteristics of the Nifti-1 but, as reserve for some header fields the
double precision, comes with a header of 544 bytes [19].
doi:10.1007/s10278-013-9657-9
doi:10.1007/s10278-013-9657-9
This project aims to offer easy access to Deep Learning for
segmentation of structures of interest in biomedical 3D scans. It
is a system that allows the easy creation of a 3D Convolutional
Neural Network, which can be trained to detect and segment
structures if corresponding ground truth labels are provided for
training. The system processes NIFTI images, making its use
straightforward for many biomedical tasks.
https://github.com/Kamnitsask/deepmedic
Transition
to NIFTI

Picture Archiving and Communications System (PACS)
PACS exit lessons learned report (PDF, 320Kb)
Picture archiving and communication system
A picture archiving and communication system (PACS)
is a medical imaging technology which provides
economical storage and convenient access to images
from multiple modalities (source machine types).[1]
Electronic images and reports are transmitted digitally
via PACS; this eliminates the need to manually file,
retrieve, or transport film jackets. The universal format
for PACS image storage and transfer is DICOM. Non-
image data, such as scanned documents, may be
incorporated using consumer industry standard
formats like PDF (Portable Document Format), once
encapsulated in DICOM.
A PACS consists of four major components: The
imaging modalities such as X-rayplain film (PF),
computed tomography (CT) and MRI, a secured
network for the transmission of patient information,
workstations for interpreting and reviewing images,
and archives for the storage and retrieval of images
and reports. Combined with available and emerging
web technology, PACS has the ability to deliver timely
and efficient access to images, interpretations, and
related data. PACS breaks down the physical and time
barriers associated with traditional film-based image
retrieval, distribution, and display.
wikipedia.org

Functional marker VISUAL FIELD #1

“Patterns of early glaucomatous visual field loss and their evolution over
time” http://iovs.arvojournals.org/article.aspx?articleid=2333021
research-innovation.ed.ac.
uk

8 September 2016
Visionary technology
Innovative optical concept offers simple,
affordable, fast glaucoma screening test
The new Viewi optical concept developed by Cambridge Consultants shows how it could be
possible for patients to monitor any effect on their vision in the comfort of their own homes. At the
moment, patients typically have an annual optometrist or hospital check-up using a visual field
analyser. Flashing lights at varying points of the visual field test sensitivity – with the patient
pressing a button each time they see a light. The novel Viewi technology performs the same test but
at a fraction of the cost – around £20 rather than £20,000 for the clinical device.
The innovative Viewi concept has been hailed as an important advance by optics expert Chris
Dainty, a professor at University College London Institute of Ophthalmology and Moorfields Eye
Hospital.
Home > Health & Fitness > http://www.digitaltrends.com/health-fitness/viewi-glaucoma-test/
Chris Dainty, a professor at University College London Institute of Ophthalmology and Moorfields Eye Hospital, expressed
interest in wider applications of Viewi, noting that it “could provide a valuable early warning system for people at risk of
developing glaucoma, as well as patients who need to monitor the effects of the disease on their vision.” Dainty concluded, “It
could also make the static perimetry test accessible to more patients in developing countries, where expensive clinical
equipment and trained professionals are often in short supply.”
Read more: http://www.digitaltrends.com/health-fitness/viewi-glaucoma-test/#ixzz4K4Z4km4Q
Follow us: @digitaltrends on Twitter | digitaltrendsftw on Facebook
scholar.google.co.uk

Functional marker PUPILLOMETRY
http://iovs.arvojournals.org/article.aspx?articleid=2467290
doi:10.1016/j.visres.2012.07.019
http://www.medicalexpo.com/cat/ophthalmology/pupillometers-pupillographs-F-144.htm
l
The 3D model of the pupillometer based on a smartphone (left) and the configuration of the measurement
system based on the prototype design (right)
.http://dx.doi.org/10.3807/JOSK.2013.17.3.249
businesswire.comdoi:10.1364/AO.53.000H27
BOSTON — A simple, quick test performed with a pupillometer appears to be an effective
screening tool for acute mild traumatic brain injury, US Army investigators report.

OCT Market #1
Peer-reviewed publications focused on OCT from 1991 to 2015. The commercial release of
products is often a catalyst for publications as clearly seen in ophthalmology and cardiology.
(Note that while some specialty-focused OCT products were introduced earlier than indicated
here, none made large-scale sales, and some companies that launched those products no
longer exist.)
Revenue from the sale of OCT systems (including biometry) in 2015 is estimated at ~$750
million/year (www.octnews.org), and ~100 companies now supply OCT systems or
components. Cumulatively since the 1996 release of the first commercial product, total OCT
system revenue (not including components) has likely exceeded ~$5.2 billion.
http://www.laserfocusworld.com/articles/print/volume-52/issue-06/
Despite the saturation of the OCT ophthalmology market in
developed countries, the OCT market for healthcare and life
science is still expected to grow from around € 500M in 2013 to
around € 1b in 2019. Strong demand from new biomedical
applications, continuous development of innovative technologies,
strong demand from developing countries will drive the OCT
market growth.
tematys.fr
The optical coherence tomography segment has been further divided into three sub-segments, namely, time-
domain OCT, Fourier-domain OCT and full-field OCT. In 2012, the optical coherence tomography (OCT)
segment held the largest share (76.5%) in the global optical imaging market followed by hyperspectral imaging
(HSI). Among the three sub-types of the OCT, Fourier-domain OCT held the largest share. Photoacoustic
tomography is expected to be the fastest growing segment in the global optical imaging market during the
forecast period 2014 to 2020.
Some of the major driving factors for the growth of this market are increasing prevalence of various disorders
related to different anatomical areas, shift in lifestyle, aging population, and increasing awareness and acceptance
of several optical imaging technologies. Changing lifestyle such as, sedentary sitting work style, longer working
durations and refraining from exercises and physical activities are leading to a number of health problems among
today’s population all over the world. Several diseases such as diabetes, hypertension etc. are also becoming
highly prevalent across the world and even in low to medium income countries.
http://www.transparencymarketresearch.com/optical-imaging-market.html

OCT Market #2
The medical markets (ophthalmic, surgical, dental, and
therapeutic) for lasers continue to grow, evidenced by
continuing expansion in the BiOS Symposium at
SPIE Photonics West 2013 as well as the financial
statements of companies like Carl Zeiss Meditec (Dublin,
CA), which saw revenues grow 13.6% for the nine-month
period up to 3Q12 and celebrated the installation of its
10,000th Cirrus HD-OCToptical coherence tomography
system in November 2012 (introduced in 1996).
laserfocusworld.com/articles/print/volume-49/issue-01 http://www.sweptlaser.com/OCT-market
The OCT market was born with the introduction of
the first ophthalmic OCT system in the '90s.
Today, OCT products are shipping in opthalmology,
dermatology, cardivascular, peripheral artery
disease, and pearl inspection. Companies are
readying produjcts for dental, esophageal, and
OCT-guided surgery. Several other areas are in
development.
The market is just over $1B in 2012, and it is
expected to grow by 18–30% per year for the
foreseeable future.
Additional general information:
Some Historical Statistics of Academic Public
ations in the Field of Optical Coherence Tomo
graphy
Optical Coherence Tomography News
(October 13, 2012)
Some Historical Statistics on Companies in t
he Market of Optical Coherence Tomography Sy
stems
Optical Coherence Tomography News
(November 3, 2012)

OCT Market #3
Today, commercial OCT systems are used regularly as an imaging standard in ophthalmology, cardiology, dermatology,
and general research. NinePoint’s Advanced OCT technology, licensed from the Wellman Center for Photomedicine at
Massachusetts General Hospital, initially focuses on esophageal and general imaging of tissue microstructure.
http://www.ninepointmedical.com/
12/03/2014 - Posted by Lee Dubay - Associate Editor, BioOptics World
Lux Research (Boston, MA) reports that the clinical optical imaging market—led by optical coherence tomography (OCT)
—will rise to $2 billion in 2020, more than doubling from 2012. With a market share of over 60 percent, OCT is expected
to retain its dominant position as it builds on ophthalmology applications while expanding into cardiology,oncology, and
gastroenterology. Other technologies such as near-infrared spectroscopy (NIRS) and photoacoustic tomography (PAT)
also hold high potential for growth, while applications such as real-time optical biopsies and surgical guidance will grow
into multi-billion-dollar segments over the long term.
Lux Research analysts examined 16 technology developers in the optical imaging industry, and evaluated them on the
Lux Innovation Grid. Among its findings:
● Leaders ready to deliver to market. Mela Sciences, Bioptigen, and Infraredx are the leaders in
novel clinical optical imaging technologies, rated “Dominant” on the Lux Innovation Grid. Each has
achieved FDA and CE clearances, allowing it to market its products in the United States and
Europe.
● Cost-cutters show potential. Typical OCT systems, the size of a medical cart, cost on average
between $80,000 and $250,000. Compact Imaging, rated "High Potential," is working to make
these devices more affordable by turning toward solid-state components instead of discrete optical
elements.
●
Multimodal systems are long-term targets. While investors looking to enter the OCT market can use
acquisitions to gain immediate market share, their longer-term focus should be on firms developing
multimodal systems such as PAT. These systems combine the most attractive features of
constituent technologies—for instance, the penetration depth of ultrasound with resolution of optical
systems—and potentially address much larger markets.
The report, titled "Advancing Clinical Imaging Beyond the Existing Standard of Care: Evaluating New Optical Imaging
Modalities," is available for download at https://portal.luxresearchinc.com/research/report_excerpt/18129.
Optical coherence tomography expected to propel
clinical optical imaging market to $2B in 2020

OCT Market #4
Research laboratories serve as the first proving ground for
commercial OCT platforms. In a recent Optics Letters paper,
professor of biomedical engineering Xingde Li and colleagues
Wu Yuan and Jessica Mavadia-Shukla at Johns Hopkins
University (Baltimore, Md., USA) investigated how to optimize
the operational conditions for an ultrahigh-resolution (UHR) OCT
system in the spectral domain using a supercontinuum source
for endoscopic imaging. A supercontinuum source enables a
broader spectral bandwidth, which supports higher
resolution imaging. In their work, Li’s team used a SuperK
Extreme low-noise supercontinuum (SC) source, a compact
tunable laser from NKT Photonics (Birkerød, Denmark). They
were able to optimize the broad bandwidth (approximately
246 nm) around a central wavelength of 800 nm and use an
appropriate power from the SC source to achieve shot-noise-
limited operation in an SC-based UHR-OCT system.
“Because the cost of OCT hardware has been so high,” says Kemp,
“it can be hard to get commercial traction. Ophthalmology has a
large installed base that enables a reasonable profit margin. In
cardiology, the profit comes from the annual sale of thousands of
disposable catheters that go along with the OCT system. But in
other markets, the cost must be low at the component level to
reduce the initial cost barrier.” Axsun decreases the cost by
integrating very small, lower-power laser beams with Ethernet-
based data acquisition electronics.
Another shifting paradigm is the size of OCT platforms. The typical
large, bulky, roll-around console is expensive in terms of
infrastructure and workflow complexity needed to obtain an OCT
image, according to Kemp. “The system has to be rolled in, plugged
in, booted up, and it takes up a large area in the office,” said Kemp.
Incorporating their more integrated, low-power, laptop- and tablet-
ready laser beam component, Axsun demonstrated a one-off SS-
OCT system at the Photonics West 2016 conference in February
that is briefcase-sized, battery-powered and pointing to the future.
The demo featured a 100-kHz swept laser (customizable to either
1060 or 1310 nm), a hand-held probe, and scan rates of more than
125 frames/s. Just as new types of ultrasound equipment are
becoming more shoebox-sized with the same level of image quality,
smaller OCT platforms seemed destined to follow.
SD-OCT is a solid technology with room for market growth, but it
has its challenges. The SD-OCT platform uses a wide wavelength
band to reflect off the different layers in the eye. The deeper parts
of the eye are imaged with the higher-frequency interference
fringes, which are more difficult to detect, producing a lower-
signal-to-noise image of the deeper structures.
In the past year or two, according to Nate Kemp, market
development manager at Axsun Technologies (Billerica, Mass.,
USA), another OCT technology has begun to make waves, shifting
the attention away from SD-OCT: swept-source OCT (SS-OCT).
Companies are in a race to launch SS-OCT platforms that can create
high-resolution images that are deeper, wider and more detailed
than previous OCT platforms.
In comparison to SD-OCT, SS-OCT uses a tunable laser to rapidly
scan at rates of up to 100,000 A-scans/s sequentially through a
range of wavelengths to assemble the B-scan cross-sectional image,
often in a fraction of a second. The optic nerve head, sclera, vitreous
and choroid structures can be imaged all in one fast, detailed scan.
While SD-OCT systems are typically based on a central laser
wavelength of 840 or 850 nm, swept-source tunable laser systems
are centered around 1 µm (for example, at 1030, 1050 or 1060
nm), which penetrates tissue more deeply than 850 nm. The longer
wavelengths can penetrate opaque media such as cataracts more
effectively, and provide the most accurate biometry measurements
possible for the best selection of an intraocular lens (IOL) before
cataract surgery.
SWEPT-SOURCE OCTCOST and SIZE of OCTSUPERCONTINUUM SOURCE

OCT Market #5: What next?
Even higher laser wavelengths for better image quality?
For example in three-photon microscopy 1,700 nm seems to be
quite optimal for brain tissue penetration and image quality
The spectral response of oxygenated hemoglobin, deoxygenated
hemoglobin, and water as a function of wavelength. The red highlighted area
indicates the biological optical window where adsorption due to the body is
at a minimum. Figure from Doane and Burda (2012)
Wavelength-dependent attenuation length in brain tissue and measured laser characteristics. a, Attenuation
spectrum of a tissue model based on Mie scattering and water absorption, showing the absorption length of
water (la, blue dashed line), the scattering length of mouse brain cortex (ls, red dashed-dotted line), and the
combined effective attenuation length (le, green solid line). The red stars indicate the attenuation lengths
reported for mouse cortex in vivo from previous work. The figure hows that the optimum wavelength window
(for three-photon microscopy) in terms of tissue penetration is near 1,700 nm when both tissue scattering
and absorption are considered. Figure from Horton et al. (2013, Cited by 304 articles).https://github.com/petteriTeikari/spectralSeparability/wiki
R.F. Spaide et al. “What’s next in laser and OCT?” Rev. Opthal. (March 2013).
Direct comparison of spectral-domain and swept-source OCT in the measurement of choroidal thickness in normal eyes
Comparison of choroidal thicknesses using swept source and spectral domain optical coherence tomography in diseased and normal eyes
Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation
Yamanaka et al. (2016) "Optical coherence microscopy in 1700 nm
spectral band for high-resolution label-free deep-tissue imaging"
Ishida and Nishizawa (2012) "Quantitative comparison of contrast
and imaging depth of ultrahigh-resolution optical coherence
tomography images in 800–1700 nm wavelength region"

OCT LinkedIn
https://nz.linkedin.com/topic/optical-coherence-tomography

Innovative OCTs Horizon 2020 project
New €4.9 million European project aiming to create a tiny and low-cost system for
optical coherence tomography (OCT). Now a team led by Wolfgang Drexler from
the Medical University of Vienna is aiming to shrink the core technology to no
more than the size of a coin, primarily to diagnose eye diseases including
diabetic retinopathy and glaucoma.
According to the official abstract describing the four-year project, the plan is to exploit recent advances in silicon photonics and
CMOS electronics for a healthcare application that is expected to grow in significance as the global population ages and diabetes
becomes more prevalent. The key difference compared with datacoms applications is that in biophotonics a visible-range light source
is typically required and most silicon-on-insulator (SOI) waveguides are not compatible with wavelengths shorter than 1.1 µm.
“To this end, a novel CMOS-compatible, low-loss silicon nitride waveguide based [on a] photonic integrated circuit (PIC)
technology platform will be developed in “OCTCHIP” (short for ophthalmic OCT on a chip, project began at the start of the year
2016) and directly applied in the field of OCT for ophthalmology,” states the team, adding: “The PIC technology developed in
OCTCHIP will make a new generation of OCT systems possible with step-changes in size and cost beyond state-of-the-art. The
monolithic integration of silicon nitride optical waveguides, silicon photodiodes and electronics combined with the hybrid
integration of a III-V laser source will enable a compact, low-cost and maintenance-free solution.”
http://optics.org/news/7/6/19
cordis.europa.eu/project/rcn/199593
Supermarket OCT?
Drexler himself even envisages a time in the future when the ultra-compact technology could feature on
supermarket shelves and be purchased by consumers for self-diagnosis. “State-of-the-art OCT technology has
its limitations: it is bulky, the size of a desktop and quite expensive, costing anything in the region of €100,000 per
unit,” he said in an announcement about the project from Photonics21. “It can detect abnormalities but at the
present moment, compact, cost-effective versions that can be used outside of hospitals and in private practice in a
hand-held mode do not exist.”
However, with diabetic retinopathy now thought to be the cause of 200 million cases of blindness worldwide,
including 60 million people in Europe, there is a clear case for a lower-cost, point-of-care diagnostic platform. That
said, the retina is an extremely complex part of the body, composed of more than ten layers of tissue despite being
only 0.25 mm thick, and is very difficult to access. Drexler said that the core component under development via the
project will be no larger than the size of a 1 cent coin. “It will reduce costs and is maintenance-free,” he added.
“OCTCHIP fosters widespread use to visualize and quantify the retina in more definition, so we can
diagnose diseases better, quicker, and cheaper.”
The researcher even thinks that the core technology may extend to use in battery-powered capsules that patients
could swallow for gastrointestinal diagnosis. “Perhaps in the future this will be available in supermarkets, for
self-diagnosis" he said.
jeppix.eu
Photonic Integration in Healthcare: ‘Towards optical
coherence tomography on a chip’ by Jeroen Kalkman

Innovative OCTs Compact Imaging
"Our lenses and beam splitters are all either
consumer level items or have a direct analogue
in the consumer domain," commented Bogue.
"That means we can make a low-cost system,
while the design itself allows that system to fit
into an inherently small footprint with low
operating power.
Potential applications for a genuinely low-cost
compact OCT platform could include a
number of uses in health monitoring and non-
destructive testing."
IPIC's involvement has been key to the
company's success in miniaturizing the
technology from its initial bench-top
implementation. The first-generation
prototypes now measure about 50 x 50 mm,
and Bogue indicated that his road map
towards even smaller versions was on course
to conclude early in 2016.
https://www.crunchbase.com/organization/fp-technology#/entity
https://compactimaging.com http://optics.org/news/6/9/56

“Innovative” OCTs BioOptigen Leica→
http://www.leica-microsystems.com/products/optical-coherence-tomography-oct/

“Established” OCT Topcon
Description
Topcon’s 3D imaging and analysis functions provide invaluable pathological confirmation of progression.
With the addition of noise reducing algorithms and Infra red/3D tracking technology, the 3D OCT-2000
provides you with extremely detailed OCT images.
Fundus Images Unique to the Topcon OCT series is its integrated retinal photography function, which is
based upon its highly successful non-mydriatic fundus camera. An interchangeable (future proof) 16.2mp
digital camera acquires highly detailed images using a sub one millisecond flash at the point of OCT
capture or stand alone fundus photography if required.
Glaucoma One of many modules within Topcon’s Fastmap software; The Glaucoma module allows fully
automated disc topography, normative database comparison and total progression analysis (trend
analysis) through various screening options. Complemented by Ganglion cell analysis and anterior
chamber angle measurements the glaucoma module is a comprehensive screening tool.
Anterior segment scanning By combining both OCT scan technology with traditional photographic
imaging a variety of analysis functions and scan protocols allow for the detection and treatment of many
corneal conditions. Full corneal thickness topography and automated central thickness values are
complemented by corneal curvature topography along with high resolution imaging.
http://www.topcon-medical.co.uk/uk/products/32-3d-oct-2000-optical-coherence-tomography.html#description
http://www.topcon-medical.co.uk/uk/products/75-imagenet-i-base.html#description

TopCon Innovative OCT→
Topcon selected to join IBM Watson Health global medical imaging collaborative
IBM (NYSE: IBM) announced on June 22nd that it is forming the Watson Health medical imaging collaborative, naming Japanese ophthalmic device
manufacturer Topcon Corporation (TYO: 7732) among the sixteen partners, which includes academic medical centers, health systems, ambulatory
radiology providers and imaging technology companies. As part of this global effort, foundational members will engage IBM's "augmented intelligence"
platform, called Watson, to extract insight from a variety of structured and unstructured data sources, such as medical imaging data, electronic health
records, radiology and pathology reports, lab results, doctors' progress notes, medical journals, clinical care guidelines and published outcomes studies.
Watson, a cognitive computing system, understands natural language, reasons and learns over time.
The inclusion of Topcon as one of the collaborating members is a testament to the company's commitment to integrating medical devices, big data
analytics and cognitive computing into its products and services. It will leverage Topcon's large family of imaging devices to facilitate and optimize the
Watson training process and accelerate the development of products and services that improve the understanding, diagnosis, and treatment of eye
disease, ultimately improving patient care.
Foundational members of the collaborative include Agfa HealthCare, Anne Arundel
Medical Center, Baptist Health South Florida, Eastern Virginia Medical School, Hologic,
ifa systems AG, inoveon, Radiology Associates of South Florida, Sentara Healthcare,
Sheridan Healthcare, Topcon, UC Sand Diego Health, University of Miami Health System,
University of Vermont Health Network, vRad, and Merge Healthcare (an IBM company).
http://fortune.com/2016/06/22/ibm-watson-health-imaging-collaboration/
diagnosticimaging.com/pacs-and-informatics

Established OCT Zeiss
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2917043
/
Zeiss ForumZeiss OCT

Established OCT Heidelberg
Multi-Modality Diagnostic Imaging of
the Eye Precision, Detail, and Versatility
Multi-modality imaging with SPECTRALIS® is helping drive the development of novel
therapeutics and changing the course of patient management in eye care. Using an
upgradeable platform approach, SPECTRALIS has enhanced the role of
spectral domain OCT by integrating it with confocal scanning laser ophthalmoscopy
(cSLO). The combination of these two technologies has enabled new imaging
capabilities, such as TruTrack™ active eye tracking, and
BluePeak™ blue laser autofluorescence, providing clinicians with unique views of the
structure and function of the eye.
https://www.heidelbergengineering.com/us/products/spectralis-models/
HEYEX Networking Solutions
Heidelberg Eye Explorer, or “HEYEX™”, is the heart of all Heidelberg
Engineering instruments, such as SPECTRALIS®, HEP and HRT. The central
patient database and uniform user interface allow the review of images nearly
anywhere in the practice without compromising on tools or image quality.
HEYEX has been designed to integrate into a variety of workflows. It enables
connectivity to EHR and PACS systems via a DICOM Interface or via custom
interfaces, providing flexible solutions from a solo practice to a large clinic
environment.
https://www.heidelbergengineering.com/us/products/heyex-networking-solutions/
Based on the website, Heidelberg indeed seems to be “established” especially compared to sleek visual branding of Zeiss

Established OCT Nidek
http://www.nidek-intl.com/product/ophthaloptom/diagnostic/dia_retina/rs-330.html
https://www.opticianonline.net/features/review-nidek-rs-330-duo-oct

Established OCT OptoVue
http://www.optovue.com/
http://optovueacademy.com/ bloomberg.com/Research

Established OCT MOptim
http://www.moptim.com/en/index.php
e.g. Moptim Mocean3000/3000 Plus
Intel i5 quad-core CPU, 4G DDR RAM
Windows 7 32-bit (3GB of RAM)
NVIDIA Quadro K600, 1 GB, 0.34 TFLOPS
1 TB HDD
METHODS
Seventy-two normal subjects were included. Every subject
underwent CMT measurement twice using one of two SD-OCT
(OSE-2000, Moptim, Shenzhen, China & 3-D OCT-1000, Topcon,
Tokyo, Japan) instruments.
http://dx.doi.org/10.3980%2Fj.issn.2222-3959.2012.03.20
linkedin.com/company/moptim

Established OCT Optos
https://www.optos.com/fr-CA/retinal-imaging-products2/
touchophthalmology.com

Electronic Medical records
Depending on what you want to do, you may want to have an API
available for the image management / electronic medical record if it is
not possible to interface the vendor-specific software directly

Proprietary formatS Vendor-specific OCT files
zeiss.com
FromHuang etal.(2013):
“Scans were obtained with certified photographers to
minimize the OCT data acquisition artifacts [15], [20].
The data samples were saved in the Heidelberg
proprietary .e2e format. They were exported from a
Heidelberg Heyex review software (version 5.1) in .vol
format and converted to the DICOM (Digital Imaging
and Communication in Medicine) [21] OPT (ophthalmic
tomography) format using a custom application built in
MATLAB. “
These plugins interpret raw binary files exported from Heidelberg
Spectralis Viewing Software. They successfully import both 8-bit SLO
and 32-bit SD-OCT images, retaining pixel scale (optical and SD-
OCT), segmentation data, and B-scan position relative to the SLO
image (included in v1.1+). In addition to single B-scan SD-OCT
images, the plug-in also opens multiple B-scan SD-OCT images as a
stack, enabling 3-D reconstruction, analysis, and modeling. The plug-
in is compatible with Spectralis Viewing Module exporting raw data in
HSF-OCT-### format. Compatability has been tested with HSF-
OCT-101, 102, and 103
http://dx.doi.org/10.1016/j.exer.2010.10.009
Heidelberg Engineering Spectralis OCT RAW
data (.vol ending): Circular scans and Optic
Nerve Head centered volumes are supported
www5.cs.fau.de .. octseg/
Problem:
Vendors have their own
proprietary image management
platforms and file formats for
lock-in purposes that might
pose problems for automated
AI pipelines.
uocte Reverse-engineered file
readers in C++ by Paul Rosenthal et al.
UOCTML, Eyetec, Heidelberg, NIDEK,
Topcon, Zeiss

Electronic medical/Health record Market
Thanks to the White House's stimulus-era initiative to bring the health
care industry into the digital age, her company has grown into the
country's leading vendor of software in the
$9.3 billion electronic health records (EHR) sector. Epic pulled in $1.8
billion in 2014 and is expanding at a rate of about 1,000 new employees a
year.
But instead of ushering in a new age of secure and easily accessible
medical files, Epic has helped create a fragmented system that leaves
doctors unable to trade information across practices or hospitals.
Epic is not the only barrier to a seamless medical records system.
Thanks to legislative maneuvering by former Rep. Ron Paul (R-Texas) in
1999, the federal government can't fund any sort of system with unique
health care identification numbers.
There have been other signs that the government has had enough with
Epic. A massive contract to overhaul and modernize the Department of
Defense's health records, worth up to $9 billion over the next 18 years,
had long been expected to go to the company. But in July, the Pentagon
announced it had instead chosen Cerner to provide the software to
serve 9.5 million DOD beneficiaries. Jonathan Woodson, the assistant
secretary of defense for health affairs, stressed that the Pentagon
believed it was "very important to have a highly integrated system that is
portable. The private sector has to position itself to be more
interoperable."
motherjones.com/politics/2015/10/
Global EHR Market
http://blog.capterra.com/top-5-ehr-trends-for-2016-2/
1. More EHRs are moving to the cloud
2. More EHRs are providing patient portals
3. Telemedicine will blow up
4. EHRs will (finally) go mobile
5. Big data will reveal more connections
in US, forbes.com
Room for a lot of disruption here!
Some have been openly critical of the tactics used by EHR vendors and their ability to “lock in” customers often in a sequential‒ ‒
fashion as they churn through different EHR vendor solutions. In their eBook ‒ Hacking Healthcare authors Fred Trotter and‒
David Uhlman offered this sober assessment:
A vendor creates a system so specific, non standardized and esoteric that one and only one vendor can possibly support or‒
maintain it. The vendor then pursues aggressive sales strategies, often combined with low initial pricing. Once fully
established in the customer site, the vendor can drain the customer with ever increasing maintenance and upgrade fees.‒
forbes.com

Towards better EMR Open-sourcing?
Writing for the New England Journal of Medicine (here), Dr. David Blumenthal (former
Director of ONC) cited four significant obstacles to the rapid adoption and effective use of EHR’s by the
healthcare community.
1) The fee-for-service payment system in the United States does not financially reward the
improved quality and efficiency that health information technology makes possible.
2) EHRs in particular are complex products that are difficult to evaluate and understand. The market’s
diverse offerings vary enormously in capability and usability, and new products are burgeoning.
3) The ability to effectively transfer electronic health information between different information
systems in various institutions and practices is underdeveloped in the United States at this time.
4) Still a fourth problem inhibiting the adoption and use of health information technology is concern
about the privacy and security of digital health information. Entire new industries have arisen
using personal health information for purposes that were never anticipated by existing privacy
statutes, and these uses are not currently regulated.
That first obstacle highlights how the healthcare IT vendor landscape mirrors the very healthcare
industry it’s designed to serve. Both have been optimized around revenue and profits ‒ not safety
and quality. The other three obstacles highlight the significant difference between simply deploying
one of the 500+ EHR solutions (very high across the healthcare industry) and it’s effective use around
coordinated and continuous patient care with better outcomes.
forbes.com/sites/danmunro/2014/07/27
The Top 5 Free and Open Source EMR Software Products
Published July 7th, 2016 by Cathy Reisenwitz in EMR
1) Practice Fusion
2) OpenMRS
3) iSALUS EHR
4) VistA
5) FreeMED
softwareadvice.com

Towards better EMR Efficiency issues
Medscape Medical News
Survey: EHRs Have Taken Over, Except for Hearts and Minds
Robert Lowes, August 30, 2016
EHRs Not Reliable for Legal Cases, Experts Say Designed to maximize
billing, EHRs have features that lend themselves to gaming and concealment,
making them equivalent to 'hearsay' in court.
The paper cites a 2012 letter from Attorney General Eric Holder and then–Secretary of
Health and Human Services Kathleen Sebelius warning hospitals not to manipulate
electronic records for the purposes of getting improper payments from Medicare. This
letter followed press reports about the widespread practice of cutting and pasting past EHR
notes into current ones.
J Am Med Inform Assoc. 2005 Sep-Oct; 12(5): 505–516.
doi: 10.1197/jamia.M1700, Cited by 710 articles
Lise Poissant, PhD, Jennifer Pereira, MSc, Robyn Tamblyn, PhD, and Yuko Kawasumi, MSc
http://dx.doi.org/10.13063%2F2327-9214.1176
http://dx.doi.org/10.1177/1460458214534091
http://www.ncbi.nlm.nih.gov/pubmed/17299922
The experts also observe that not all EHRs are capable of performing audits that show when entries were made and whether and when they were changed. They
cite a US Department of Health and Human Services Office of Inspector General report that stated that 44% of hospitals could delete their EHR audit logs.
Neither Dr Leone nor George L. Paul, an Arizona attorney who has written a book on digital evidence, could say whether the reliability of EHRs has been a factor
in any malpractice case to date. But Paul noted that the widespread use of EHRs is still new and that it can take several years for an issue such as this to be litigated,
adjudicated, and appealed to higher courts
Ave Maria Law Rev. 2014;12:257-289. Full text

US Centers for Medicare and Medicaid Services (CMS) and the Office of the
National Coordinator for Health Information Technology (ONC) published the
EHR Incentive Program and Health IT Certification Program final rules. The final
rules emphasize simplicity and flexibility, encourage greater interoperability, and
allow stakeholders additional time to phase in the required changes. Health care
providers, developers, and consumers may benefit from the changes included in
the final rules:
deloitte.com/us/en/pages/life-sciences-and-health-care
Towards better EMR US Issues
As a part of the American Recovery and Reinvestment Act, all public and private healthcare providers and
other eligible professionals (EP) were required to adopt and demonstrate “meaningful use” of electronic
medical records (EMR) by January 1, 2014 in order to maintain their existing Medicaid and Medicare
reimbursement levels. Since that date, the use of electronic medical and health records has spread worldwide
and shown its many benefits to health organizations everywhere. “Meaningful use” of electronic health
records (EHR), as defined by HealthIT.gov, consists of using digital medical and health records to achieve the
following:
●
Improve quality, safety, efficiency, and reduce health disparities
●
Engage patients and family
●
Improve care coordination, and population and public health
●
Maintain privacy and security of patient health information
The American Recovery and Reinvestment Act also included financial incentives for healthcare providers who
prove meaningful use of electronic health records (EHR). EHR is not only a more comprehensive patient history
than electronic medical records (EMR), the latter of which contains a patient’s medical history from just one
practice, but was also the end-goal of the federal mandate.
Penalties were also issued to those healthcare organizations that were non-compliant. For example, EP’s who
didn’t implement EMR/EHR systems and demonstrate their meaningful use by 2015 experienced a 1%
reduction in Medicare reimbursements.
Not surprisingly, the EMR/EHR mandate spurred significant growth in health informatics, an
interdisciplinary field of study that merges information technology and healthcare. Healthcare professionals
with the skills and knowledge necessary to develop, implement, and manage IT software and applications in a
medical environment are already in high demand, and the field is expected to experience continual growth.

Healthcare cutting EHR costs
By Peter Groen, and Roger A. Maduro | July 3, 2013: “Cost transparency is obviously a big issue in the healthcare industry. … , or the costs associated with
acquiring and implementing an Electronic Health Record (EHR) system for a hospital - Why are all these costs often carefully hidden? Is there something
special about the healthcare industry that says – "Let's not talk about how much things really cost."
In a Forbes Magazine article written by Zina Moukheiber and titled "
The Staggering Cost Of An Epic Electronic Health Record Might Not Be Worth It," Moukheiber
states that "not-for-profit hospitals or healthcare provider organizations will need every penny
of those taxpayers’ dollars [in meaninful use reimbursements], but they won’t cover anywhere
near the staggering cost of an Epic EHR." Moukheiber points out "Duke University Health
System will shell out $700 million, so will Boston's Partners Healthcare; University of
California in San Francisco will pay $150 million," adding that "Customers, such as New
Hampshire’s Dartmouth-Hitchcock Medical Center are feeling the pinch. DHMC which
implemented Epic last year at a cost of $80 million, expects a weak operating performance
in 2012, partly because of expenses related to Epic."
According to a recent article from KTOO News, "the owners of Juneau, Alaska-based
Bartlett Regional Hospital voted recently to break their contract with Cerner for a new
system, rather than incur the expense of implementation." Bartlett is one of the latest U.S.
hospital to run into financial trouble while installing a new EHR system.
Implementation of the new EHR would have cost $7.37 million, plus an annual
maintenance fee of $1.155 million. Bartlett Regional Hospital is a small 48 bed acute
care hospital, with a 12-bed Adult Behavioral Health Facility, and a 16-bed Chemical
Dependency Recovery Center.
A recent FierceEMR article, titled "EHR transition may be financially risky for hospitals"
reports that "Henry Ford Health System's investment in an Epic system was a major factor
in its 15 percent decrease in net income from 2011 to 2012. Wake Forest Baptist Medical
Center also reported that its adoption of a new Epic system caused it to suffer
unanticipated losses and business cycle disruptions."
In an article titled "The Costly Darkside Of EMR Implementations," Dr. Edmund Billings
points out that the West Virginia Department of Health & Human Resources spent $9
million to acquire and implement the VistA open source EHR system across its 7
hospitals that operate 776 beds in total. Contrast this with the $92 million spent by West
Virginia University (WVU) to acquire and implement a proprietary EHR system in its 7
hospitals that operate 526 beds.
In a recent article in Open Health News, Dr. Billings details the catastrophic financial
situation facing the 9-hospital Maine Health system in the State of Maine as a result of
their Epic Implementation. While the often cited cost figure for this Epic Implementation is
$160 million, Dr. Billing does a financial analysis that shows that the real cost is going to
be almost $370 million over the next five years. In a comment to the article, our own
Roger A. Maduro points that even this figure is a vast underestimate. Maduro points out
that Billings missed the cost of licensing upgrades imposed by proprietary EHR vendors
every 18 to 36 months. Adding those "licence upgrade" costs and projecting over six years
(to account for two "upgrade" cycles), the projected cost for MaineHealth's EHR is $600
million. It should be noted that the Maine Health hospital system is very similar to West
Virginia's, which, as noted in the note above, implemented the open source VistA
system at a cost of only $9 million.
When it comes to health data interoperability, there are two schools of thought. The first believes
that since there are so many systems already in place, we should agree on the exchange format (and
recently the APIs) and convert the proprietary data into that format as needed. This is the preferred
approach today when faced with a large installed base of megasuite solutions. The focus here is on
the applications.
The second approach is standardizing the health data first, building the new systems on top and
avoiding the interoperability issues altogether. Sure, sometimes this is not possible. But when looking
at projects spending billions of dollars, Euros or pounds on new systems, I firmly believe it is a much
wiser path to take. For a detailed discussion on the two approaches, see Wolandscat blog.
It would also prevent vendor lock-in by making applications easier to replace. Due to the costs of
data migration, introducing a new application often means starting fresh with a nearly empty
database. This is true even of well funded projects like the ongoing US DoD EHR implementation. I
tried to show why in healthcare, data is more important than applications. Now let’s see what
defines the data layer (More details are available here):
●
First, a set of common, open application programming interfaces (APIs). They need to cover
the breadth of health data required by health applications, including systems as complex as EHRs.
They need to be vendor-neutral and technology neutral as well.
●
Second, common, open data models that are multi-lingual, comprehensive, semantically correct
and modeled by clinicians. They need to be governed, enable versioning and querying. This is best
achieved if they support multi-level modeling, where an underlying maximal data set can be
constrained and customized for a specific use case at a higher level of the model.
●
Third, support for clinical terminologies ensuring the computability of health data.
●
Finally, all of the above specifications must be published and freely available for use.
http://www.openhealthnews.com/story/2016-05-04/postmodern-ehr-data-layer
http://www.openhealthnews.com/articles/2013/ehr-systems-cost-transparency-healthcare-industry

Practice Fusion
fortune.com/2015/08/19
http://www.practicefusion.com/electronic-health-record-ehr/
San Francisco-based Practice Fusion, the largest U.S. cloud-based electronic health record
(EHR) platform for doctors and patients, on June 18th launched a native version of its EHR
optimized for iOS and Android based tablets.
Data from the recent AmericanEHR study in partnership with the American College of Physicians
(ACP) demonstrated that Practice Fusion is the clear leader for solo and practices with 1-3
medical providers. The study found that for solo providers, Practice Fusion has 40% greater
market share than eClinicalWorks, the next largest competitor in this category.
According to Healthit.gov, a federal website that provides comprehensive up-to date information
regarding EHRs, the average upfront cost of implementation of an EHR is $33,000 per
provider, along with an annual maintenance fee of $4,000. For many smaller practices, this may
not be feasible—thus making the case for implementing Practice Fusion, a completely free,
Meaningful Use certified EHR.
forbes.com
softwareadvice.com
https://www.crunchbase.com/organization/practice-fusion#/entity

OpenMRS
https://github.com/openmrs
http://openmrs.org
Software is available under the
Mozilla Public License 2.0 with Healthcare Disclaimer (MPL 2.0 HD).What technologies is OpenMRS built on?
OpenMRS is programmed in Java and the core application works through a web-browser. Hibernate is
used as an interface layer to the database. Tomcat is used as the web application server. The back end
database is currently in MySQL. The system creates XML schemas for form design. Form design and
form data entry is currently done in Microsoft Infopath, HTML, or XForms. When form data entered is
submitted, it is converted into a HL7 message before going into the database.

OpenMRS Build on top of it
Bahmni is an easy-to-use EMR & hospital system. It combines
and enhances existing open source products into a single
solution.
What is the difference between Bahmni and other OpenMRS
based applications?
OpenMRS is a great platform on which many have developed end user EMR
applications. Most of these applications are by design specific to a particular disease,
one type of hospital in a country or for just one hospital. Bahmni is aimed to being a
generic system which can be used for multiple diseases, hospitals in different
countries (at different levels) - only via configuration and not via software
development. The EMR part of Bahmni complements OpenMRS platform (or backend)
to provide an end user system.
What is the cost / fees of Bahmni?
Bahmni is Open Source Software licensed under AGPL 3.0 so there are no license fees
to be paid. It can be downloaded and used at no cost. The details of license can be
found here. You can implement Bahmni yourself or engage an implementer.
Implementer may charge a fee for implementation services including installation,
configuration, data migration, reference data set up, user creation, training, go-live
support and support.
http://www.bahmni.org/faq/
http://www.bahmni.org/ Using OpenMRS, Odoo, dcm4chee, OpenELIS
https://github.com/openmrs/openmrs-core
https://www.google-melange.com/archive/gsoc/2014/orgs/openmrs
Google Code-In | Promo Video on OpenMRS Project - YouTube
https://wiki.openmrs.org/display/docs/Developer+Guide

OpenVista (VistA)
VA’s ComputerizedProvider Record System (CPRS),
thecore electronicrecordinthebroader VistA platform
In their 2014 EHR Report—a survey of 18,575 physicians on their EHR preferences—Medscape concludes
that doctors like using the VA’s Computerized Provider Record System (CPRS), the core electronic record
in the broader VistA platform, more than any other solution.
The highest-rated EHR, with a score of 3.9, is the Veterans Administration EHR: VA-CPRS. It’s regarded as
one of the best overall by our physician respondents.
So, why is VistA CPRS the preferred choice? In a word, design. The VA built the system with two design
goals: improved patient care and rapid adoptability. Physicians at the VA rotate through services and the
system has to be adoptable with minimal (2 hours) training; they learn it as they take care of patients.
Maybe you’re wondering how a government-derived software system could be more highly rated than
private sector alternatives. As mentioned above, the VA’s goals are to develop a system that improves care
for veterans and is easy to learn. Contrast that with the natural overarching goals of proprietary EHR
providers, which is to automate the enterprise and make money.
So, if VistA is the preferred choice, why is adoption of VistA-derived systems outside the VA so low?
One explanation is lack of awareness. How many hospitals and clinics know that VistA code is public
domain and available without expensive license fees? That private companies are succeeding by offering
development and support for VistA-based solutions?
Well, for large academic medical centers and cash-rich nonprofit (cough, cough) healthcare systems, it may
be true. I mean, who would ever stand in the way of a hospital’s right to overpay for Epic?
Edmund Billings, MD, is chief medical officer of Medsphere Systems Corporation, the solution provider for the OpenVista electronic health record.
http://hitconsultant.net/2014/07/24/physicians-prefer-the-vas-ehr/
http://www.medsphere.com/open-vista
OpenVista Technology
Medsphere's Enterprise Assessment process determines each hospital's unique
requirements and presents alternatives for meeting those goals. Our collaborative
approach answers questions about hosting, server virtualization and other issues before
an implementation plan commences.
Hardware and Operating System
A client/server solution, OpenVista runs effectively on a single Linux or Windows server,
even in large hospitals. Workstation requirements are modest, enabling many OpenVista
client sites to save money by using existing Windows workstations.
Database
OpenVista requires either InterSystems Caché (Linux or Windows) or FIS GT.M (Linux
only) database management systems. Caché is a commercial product that supports some
of the largest hospitals and EHR implementations in the USA. GT.M is an open source
product with professional support that runs both hospitals and some of the largest
banking systems in the world. Caché and GT.M each have a long track record of
exceptional reliability and performance in healthcare scenarios.
Network
The bandwidth requirements for OpenVista client operations are modest. A reliable DSL
or cable connection is adequate for a small satellite clinic; to accommodate more users
and interfaces, a larger site, such as a hospital, will require more bandwidth.
http://www.medsphere.com/open-vista/technology
healthcareitnews.com

Open-source Eye EMR
OpenEyes is a collaborative, open source, project led by
Moorfields Eye Hospital. The goal is to produce a
framework which will allow the rapid, and continuous
development of electronic patient records (EPR) with
contributions from Hospitals, Institutions, Academic
departments, Companies, and Individuals.
https://github.com/openeyes/OpenEyes
●
Development of 3rd party applications would be simplified by
standardized open source platforms of which is an example of the
OpenEyes initiative led by Moorfields Eye Hospital in London, UK

Interoperability
https://youtu.be/0E121gukglE?t=26m36s
https://deepmind.com/health

EHR/EMR Clouds
https://eyenetra.com/product-insight.html
http://zhhealthcare.com/
dailyinfographic.com

MEDICAL File transfer
vigilantmedical.net
nuance.com
http://www.dicomgrid.com/product/share
http://www.intelemage.com
businesswire.com

Health care So what is wrong with it?

Healthcare general model
The NHS Structure (UK)
The NHS is divided into two sections: primary and
secondary care. Primary care is the first point of contact
for most people and is delivered by a wide range of
independent contractors, including GPs, dentists,
pharmacists and optometrists.
Secondary care is known as acute healthcare and can be
either elective care or emergency care. Elective care
means planned specialist medical care or surgery,
usually following referral from a primary or community
health professional such as a GP.
http://dx.doi.org/10.1007%2Fs11606-010-1464-0
Primary healthcare results in better health outcomes, reduced health disparities and
lower spending, including on avoidable emergency room visits and hospital care. With
that being said, primary care physicians are an important component in ensuring that the
healthcare system as a whole is sustainable. However, despite their importance to the
healthcare system, the primary care position has suffered in terms of its prestige in
part due to the differences in salary when compared to doctors that decide to specialize.
In a 2010 national study of physician wages conducted by the UC Davis Health System
found that specialists are paid as much as 52 percent more than primary care physicians,
even though primary care physicians see far more patients.[10]
Primary care physicians earn $60.48 per hour; specialists on average earn $88.34.
[10] A follow up study conducted by the UC Davis Health System found that earnings
over the course of the careers of primary care physicians averaged as much as $2.8
million less than the earnings of their specialist colleagues.[11]This discrepancy in pay has
potentially made primary care a less attractive choice for medical school graduates.
https://en.wikipedia.org/wiki/Primary_care#United_States
The Continuum of Healthcare
OJIN: The Online Journal of Issues in Nursing
Rising to the Challenge of Health Care Reform With Entrepreneurial and Intrapreneurial Nursing Initiatives
Anne Wilson, PhD, MN, BN, FRCNA; Nancy Whitaker, BPsych (Hon); Deirdre Whitford, PhD

Healthcare in US
●
It is well-known that the healthcare in US is the least cost-efficient system in Western
countries even though they have the access to cutting-edge technologies and high-level
medical universities.
By Lillian Thomas, Pittsburgh Post-Gazette, June 14, 2014 3:15 p.m.
archive.jsonline.com/news
The U.S. health care system is neither a true market system, nor a government
managed system. It's complicated and hard to navigate. The same forces that make it
a bloated drain on the economy drive it out of poor neighborhoods where it's sorely
needed.
Princeton University health economist Uwe Reinhardt compares the system to one
where employees are told they'd be reimbursed for clothing deemed "necessary"
and "appropriate" for the job, but are forced to shop blindfolded, stuffing items into a
cart without knowing what they cost or what they look like — and only informed
months later whether they'd be reimbursed.
https://en.wikipedia.org/wiki/Health_care_in_the_United_States
https://en.wikipedia.org/wiki/Health_care_in_Canadaforbes.com/sites/robertpearl/2014/01/09

US Fee-For-Service model
This article is part of a series of blog posts by leaders in health and health care who participated in
Spotlight Health from June 25-28, the opening segment of the Aspen Ideas Festival.
Current Fee-For-Service
Primary care fee-for-service only pays a doctor for a certain set of
discrete activities—largely confined to doctor sick visits—which are tiered
by means of an arcane coding system counting very discrete micro tasks,
such as how many organ systems a doctor examines, or what questions a
doctor asks a patient about the quality of their symptoms.
This encourages every health care issue or question to become a doctor visit
(because that is paid for), and for the doctor to do most things instead of
others on the team (because that is what is paid for). It leads to reactive
care (since thinking of a patient not in front of you isn’t paid for), and leads
to framing the job as taking care of one patient at a time, like a never-
ending series of widgets on an assembly line.
Electronic health records, not surprisingly, are thus built to optimize this
fee-for-service payment, particularly the coding level of each visit, and leads
to lots of useless points and clicks and incredibly long notes that, in
retrospect, are extremely difficult to comprehend. And practices spend a
huge percent of their time and overhead dealing with all of this, which is
really just a game, and does not lead to one iota of better patient care.
Fee-for-service is simply the wrong model to pay for primary care. It is
toxic to good care and to physician and team culture, so we should simply
stop using it, not try to supplement it. Primary care should be about
continuous healing relationships, and discretely paying for services is
antithetical to this.
healthaffairs.org/blog/2015/08/17
If fee-for-service is a problem, what's the solution?
By Paul Demko | February 25, 2015
http://www.modernhealthcare.com/article/20150225/NEWS/150229939
HEDIS® and Quality Compass®
HEDIS is a tool used by more than 90 percent of America's health plans to
measure performance on important dimensions of care and service. Because so
many plans collect HEDIS data, and because the measures are so specifically
defined, HEDIS makes it possible to compare the performance of health plans on
an "apples-to-apples" basis. Health plans also use HEDIS results themselves to see
where they need to focus their improvement efforts.
http://www.ncqa.org/HEDISQualityMeasurement/WhatisHEDIS.aspx
Advanced Laboratory Analytics — A Disruptive Solution for Health Systems
Eleanor Herriman, MD, MBA
Chief Medical Informatics Officer

US Healthcare Misaligned incentives #1
U.S. Health Care Rewards Quantity Over Quality
Imagine you’re planning to remodel your kitchen. You hire a
contractor and opt to defer entirely to his judgment on the
kitchen’s aesthetics and the source of his materials.
Instead of requesting a competitive bid or choosing exactly what you
want, you agree to a time and materials contract. By the end of
the remodel, the contractor has billed more hours than you
expected, marked up the cost of the materials and charged you
twice for his construction errors.
Chances are you’d never agree to such a lopsided arrangement
for your kitchen. But that’s the approach most Americans take
when they go for medical care.
The U.S. health care system pays physicians based on a
fee-for-service (FFS) financial model. In short, it’s that “time and
materials” contract you’d never agree to for your kitchen. But it’s
not the doctors or hospital administrators who are the
fundamental problem. It’s the financial model.
forbes.com/sites/robertpearl/2014/01/09
Fee-For-Service Model Pervasive Yet Perverse
Economics 101 teaches that as supply goes up, costs should come down. But this tenant doesn’t
hold true in medical care – not when the supplier also controls demand. In health care, doctors
can stimulate demand because (a) health insurance blinds most patients to the costs of services
and (b) patients often don’t know whether a complex procedure is as necessary as a non-invasive
one.
Over the past 15 years, U.S. medical school enrollment has risen by 30 percent. But while the
number of specialty residences – and therefore specialists in a community – has grown
substantially, the number of primary care residents has remained flat.
The reason is simple: Hospitals receive the same financial reimbursement from the federal
government whether they train a primary care physician or an orthopedic surgeon. The
orthopedic resident will earn the hospital a lot of money while the primary care physician will
bring in little or nothing. As a hospital administrator, which clinical training program would you
expand?
All too often, patients acquire any number of conditions from a hospital stay, from pressure ulcers
to post-admission infections. In fact, about 4 percent of beds in a typical hospital are occupied by
patients who couldn’t be discharged because of a hospital-acquired complication.
As an example, a recent study found that privately insured surgical patients with one or more
complications provided hospitals with a 330 percent higher profit margin than those who had
no complications.
Free Rein Drives Personal Gain Just like the contractor who’s given free rein over a kitchen
remodeling project, little limits doctors from making decisions that boost their bottom line.
Oncologists routinely purchase the chemotherapy they administer, mark up the price
substantially and keep the difference for themselves. Surgeons often buy into ambulatory surgery
centers or “surgicenters,” and earn guaranteed double digit returns provided they commit to
bringing their fully insured patients there. Meanwhile, drug and device companies pay physicians
to talk up new medications or devices. And until the passage of the Sunshine Act, the U.S. health
care system didn’t require any visibility or disclosure.
forbes.com/sites/robertpearl/2014/01/09According to the MGMA report, the average primary care physician in a
group earned $171,519 last year, up 2 percent. Specialty care physicians
earned $322,259, up 1.8 percent. Over the last five years, incomes rose
11.9 percent for all primary care doctors, while specialists’ pay surged 17.3
percent
managedcaremag.com/archives/2007/12.

US Healthcare Misaligned incentives #2
The principal-agent problems in health
care: evidence from prescribing patterns
of private providers in Vietnam
The principal-agent problem in health care
asserts that providers, being the imperfect
agents of patients, will act to maximize their
profits at the expense of the patients’
interests. This problem applies especially
where professional regulations are lacking
and incentives exist to directly link providers’
actions to their profits, such as a fee-for-
service payment system.
http://dx.doi.org/10.1093/heapol/czr028
http://dx.doi.org/10.2307/1912246
Cited by 3325
http://dx.doi.org/10.1016/0167-6296(93)90023-8
Cited by 181
NBER Working Paper No. 21930
Issued in January 2016
NBER Program(s): AG HC HE
A longstanding literature has highlighted the tension between the altruism of physicians
and their desire for profit. This paper develops new implications for how these competing
forces drive pricing and utilization in healthcare markets. Altruism dictates that providers
reduce utilization in response to higher prices, but profit-maximization does the opposite.
Rational physicians will behave more altruistically when treating poorer patients or those
that face higher medical cost burdens, and when foregone profits are lower. These insights
help explain the observed heterogeneity in pricing dynamics across different healthcare
markets.
http://dx.doi.org/10.3386/w21930
http://dx.doi.org/10.1097/HMR.0000000000000042
Physician financial conflict of interest is a concern in the delivery of medicine because of its possible influence on
the cost and the quality of patient care. There has been an extensive discussion of the ethical, economic, and legal
aspects of this issue but little direct empirical evidence of its magnitude or effects.
Results indicate that the vast majority of physicians receive industry gifts in various forms, and the receipt of
gifts is associated with lower perceived quality of patient care. There is also an inverse relationship between the
frequency of received gifts and the perceived quality of care. Physicians need to be aware of the widespread
receipt of industry gifts in medical practice and the potential adverse impact of such receipts on the delivery of
care.

US towards value-based medicine #1
Xerox surveyed 761 U.S. adults and 204 healthcare payers and providers to gauge their
attitudes about the current state of health care. To view the full results, go to
http://www.xerox.com/healthcaresurvey
http://www.thechicagodoctor.com/columns/healthcare-it-2/value-based-medicine-success/
http://blog.academyhealth.org/movingawayfromffs/
Lisa McDonnel, SVP, Network Strategy & Line of Business Support,
UnitedHealthcare Networks http://slideplayer.com/slide/4895830/
As part of health reform and confronting a health care system with costs that are unsustainable, policymakers and
others have been looking at – and testing – new payment models. These would move away from fee-for-service and
payment based on volume to systems that encourage more coordinated care, focusing on the overall health of the
population.The graph below shows the incentives under the current payment model and the evolution of the
incentives as we shift from fee for service to population health.
http://www.dartmouth-hitchcock.org/about_dh/new_reimbursement_models.html

by COURTNEY BAIRD March 08, 2016
At all stages of life – from infancy to old age – the researchers discovered
consistently poorer health in the US for every measure. Some of the more
remarkable findings include:
• The highest rate of women dying due to complications of pregnancy and childbirth;
• The highest chance that a child will die before age 5;
• The second-highest rate of death by coronary heart disease;
• The second-highest rate of death by lung disease;
• Some of the worst rates of heart disease, lung disease, obesity and diabetes.
A plethora of other reports by groups such as the World Health Organization
and the OECD reach similar conclusions.
Todd Hixon , CONTRIBUTOR
I blog about entrepreneurs, their world, and the new, new thing.
forbes.com/sites/toddhixon/2015/06/11
Healthcare and government leaders talk a lot about value-
based payment changing the way U.S. healthcare works. But, a
closer look shows that “value” is often measured on the basis
of procedures. So providers are still paid for delivering
volume. More important, value as experienced by medical
customers(Wherever possible, I use the term “customer” or
“medical customer” instead of “patient”.) is still not a big factor in
the provider incentive equation.
In primary care particularly, much of the value of good care
emerges over time. Eighty percent of healthcare cost is driven
by chronic diseases which often result from lifestyle choices:
over-eating, no exercise, sun-bathing, smoking, excessive
drinking, bad diet, etc.
Fixing this problem broadly is a huge challenge because the
root problem is how we pay for healthcare, a gnarly political
problem. And short-term oriented behaviors occur at every
level of the healthcare payment chain: government,
corporations, and individuals. But if we don’t start to fix these
problems, then in many cases “value-based healthcare” will turn
out to be the same old pig with new lipstick.
doctorsbag.net/2015/04/16

http://dx.doi.org/10.1377/hlthaff.2015.1291
forbes.com/sites/brucejapsen/2015/01/26
http://dx.doi.org/10.1056/NEJMp1500445,
GoogleScholar: Cited by 251 times
http://dx.doi.org/10.1056/NEJMhpr1503614
http://dx.doi.org/10.1001/jama.2015.18161
President Obama is the only sitting president of the United States in modern history to publish an article
inJAMA.1 That seems appropriate since he is also the only recent president to sign comprehensive health
reform legislation, the Affordable Care Act (ACA). The president adopted a dual mandate for the ACA: it
needed not only to expand coverage but also to contain costs (despite the additional utilization associated
with the increased coverage) and improve quality.
http://dx.doi.org/10.1001/jama.2016.9876
http://dx.doi.org/10.1016/S1474-4422(16)00064-8

Emad Rizk, MD, McKesson Health Solutions
“Navigating the Complexity of New Value-Based Reimbursement Models”
http://www.slideshare.net/ClevelandHeartLab/emad-rizk-md-final
"Many of the alternative payment models currently being implemented in Medicare not only
fail to solve the problems in the current payment system, they can actually make things worse
for physicians who want to improve care and reduce spending,” stated Harold D. Miller,
CHQPR’s President and CEO.
revcycleintelligence.com
hbr.org/2013/10

US Who pays and how much?
medicaleconomics.modernmedicine.com
US Health Care Spending: Who Pays?
Josh Cothran, Georgia Institute of Technology In the past 50
years, the way health care is financed has changed, with private
payers and public insurance paying for more care. This interactive
graphic shows who paid for the nation's health care and how
much it cost.
US patients typically do not know the actual price of their medical operations,
as insurance companies typically pay the part exceeding the deductibles. One
approach to mitigate this, price transparency have been tried to introduce to
US healthcare with varying results. doi:10.1001/jama.2016.4325
citeseerx.ist.psu.edu
http://www.ncbi.nlm.nih.gov/books/NBK53906/
pwc.com/us/en/health-industries

Functional marker PUPILLOMETRY
http://iovs.arvojournals.org/article.aspx?articleid=2467290
doi:10.1016/j.visres.
2012.07.019
http://www.medicalexpo.com/cat/ophthalmology/pupillometers-pupillographs-F-144.htm
l
The 3D model of the pupillometer based on a smartphone (left) and the configuration of the measurement
system based on the prototype design (right)
.http://dx.doi.org/10.3807/JOSK.2013.17.3.249
businesswire.comdoi:10.1364/AO.53.000H27
BOSTON — A simple, quick test performed with a pupillometer appears to be an effective
screening tool for acute mild traumatic brain injury, US Army investigators report.

Eye care in US 2014 Report
Medscape Ophthalmologist Compensation Report 2014
Carol Peckham, April 15, 2014
In Medscape's 2014 Compensation Report,
ophthalmologists fall slightly above the middle among
all physicians, with average earnings of $291,000. As
in previous Medscape reports, orthopedists are the
earning leaders, followed by cardiologists. Urologists
and gastroenterologists are tied for third place.

Eye care in US 2016 Report
Medscape Ophthalmologist Compensation Report 2016
Carol Peckham | April 1, 2016
Bureaucratic tasks were the prime cause of physician burnout, according to
this year's Medscape Lifestyle Report (and in previous ones as well). Second
was spending too many hours at work. Among ophthalmologists responding to
this year's survey, 37% of those who are self-employed and 32% of their
employed peers spend 10 hours or more per week on paperwork and
administrative tasks.

Health care in Europe United Kingdom #1
The UK’s National Health Service is the best healthcare system in the world, and the US system is the worst
according to a new study by The Commonwealth Fund (direct link).
UK citizens are not necessarily more healthy than other citizens when it comes to
healthy life expectancy. The obesity crisis, adiabetes epidemic and widespread smoking
have all contributed to this.
ampp3d.mirror.co.uk/2014/06/18

Health care in Europe United Kingdom #2: NHS
www.gov.uk
●
In Europe, there has been more focus on making the system work more
efficiently as a whole instead of the piecewise optimized system of the USA.

Artificial Intelligence in Healthcare
While the biggest problems are in medical financial models, there are a
lot of room for innovations In healthcare for artificial intelligence -based
solutions.

Healthcare Artificial Intelligence #1
http://blog.ventureradar.com/2015/09/30/top-5-companies-revolutionizing-healthcare-with-machine-learning/
http://www.enlitic.com/
http://www.lumiata.com/

Healthcare Artificial Intelligence #2
forbes.com
businesskorea.co.kr
http://www.journals.elsevier.com/artificial-intelligence-in-medicine/
www.wsj.com
http://www.cnbc.com/2016/08/17/
cbinsights.com
technologyreview.com
Ali Parsa, Babylon
forbes.com

Healthcare AI companies #1
stratifiedmedical.com
babylonhealth.com

Enlitic is a startup that uses deep learning and image analysis to
help doctors make diagnoses and spot abnormalities in medical
images. For example, Enlitic could analyze medical images such
as X-rays, MRIs, or CT scans for trends in the data or anomalies
in individual images.
Cardiogram, an Apple Watch app, uses an algorithm to detect when
changes in a user’s heart rate may signal a serious health disorder.
The National Institutes of Health–funded AiCure app uses a
smartphone’s webcam and AI to autonomously confirm that
patients are adhering to their prescriptions: critical for people
with serious ailments and participants in clinical trials.
San Francisco–based startup Sense.ly has a slew of customers,
from the National Health Service to UC San Francisco, for its
virtual nurse, Molly. The interface uses machine learning to
support patients with chronic conditions between doctor’s visits.
Part of a larger effort to offer individuals targeted diagnoses
and treatments, Toronto startup Deep Genomics identifies
patterns in huge data sets of genetic information, looking for
mutations and linkages to disease.
Developing pharmaceuticals can take decades. Silicon Valley’s
Atomwise speeds things up with supercomputers that root
out therapies from a database of molecular structures.
Meanwhile, Berg Health also mines data for clues about why
some people survive diseases—insights that can inform new
therapies.
Imagen is building a world without diagnostic errors. We are
beginning by applying the latest advances in computer vision
and machine learning to medical imaging. Each year hundreds of
millions of people across the world are misdiagnosed. Diagnostic
errors are a leading cause of patient harm and unnecessary
costs, making it one of the greatest problems in healthcare.
http://www.imagentechnologies.com/
Quantified Skin. Powered by Deep Learning. Our platform
analyzes the contextual and behavioral signals of users from
images and sensor data.
Total Equity Funding: $280.75k in 3 Rounds. Most Recent
Funding: $60.75 Thousand on January 1, 2014 / Undisclosed
Round
The summit will showcase the opportunities of advancing
methods in deep learning and their impact across healthcare &
medicine. Discover the deep learning tools & techniques set to
revolutionise healthcare applications, medicine & diagnostics
from a global line-up of experts. re-work.co
Healthcare AI companies #2

3rd
party APIs for artificial intelligence
http://www.i-nside.com/
blog.clarifai.com
http://clarifai.com/

RETINAL AI Companies DeepMind #1
Say you succeed and create a super intelligence. What happens next? Do you
donate the technology to the United Nations?
I think it should be. We’ve talked about this a lot. Actually Eric Schmidt [executive
chairman of Alphabet, Google’s parent] has mentioned this. We’ve talked to him. We
think that AI has to be used for the benefit of everyone.
https://www.bloomberg.com/features/2016-demis-hassabis-interview-issue/

RETINAL AI Companies DeepMind #2
At a Glance
●
You may have heard of Google’s DeepMind division – which has artificial
intelligence (AI) algorithms that can beat not only Atari videogames from the 1980s,
but also the world’s best player of the world’s most complex board game, Go
●
But their AI platform has health applications too, which inspired Moorfields
Consultant Ophthalmologist, Pearse Keane, to assess its potential to automate and
transform retinal image analysis – and disease diagnostics
●
Moorfields is now sharing 1 million fundus photograph and OCT images with
DeepMind, who will train its AI algorithms to detect even the earliest signs of
disease pathology
●
Manual retinal image analysis today requires highly-trained, experienced
specialists, and takes time. AI could help both speed up the process, and prioritize
the patients who need review and treatment the earliest
Pearse Keane, a Consultant Ophthalmologist at Moorfields, initiated the
collaboration. Below is his story of how Moorfields and DeepMind got working
together, what they’re currently working on, and what’s next.: “I sent Mustafa
Suleyman a message on LinkedIn, and within a few days I was meeting him for coffee
to get the project underway.”
The AI can surely come to better conclusions if it knows the patient’s
history, demographics, treatment history and so on – how much data
will be used?
We’ve got two strands of approach to this. The first work is on the anonymized data,
which will provide more limited information. For example, we will provide the diagnosis
(say, AMD), the patient’s age, and this will be linked to a certain OCT scan, and
DeepMind’s algorithms will get to work – but there will be no patient identifiable
information. This is what we’re doing our initial analysis from.
We’re also planning research on pseudonymized data as well. This will include additional
labels, and in particular will involve longitudinal image sets. We’ll be able to see if a patient
has had OCT scans performed at multiple visits, meaning that we can then track the
progression of the disease. We have taken extra measures to be very confident that this
won’t allow you to identify any of the patients. We’ve got ethical approval for our
pseudonymized data set, but it’s still pending UK Health Research Authority approvals.
Do you hope the AI can eventually move beyond diagnostics?
I think in the medium- to long-term we definitely hope that it will provide new scientific
insight. In the short-term we want to see if we can get accurate diagnoses, and then we’d
like to see if we can get information about disease prognosis and pathophysiology – for
example, what is the risk of converting from dry to wet AMD, and what timeline might this
occur on?
One of the really nice things about this collaboration is that DeepMind wants the
research to be clinician-led. We don’t just give them data and leave them alone to come
up with something; it’s a two-way process. They’re always looking for guidance on what
features we’re interested in, what clinical problems will have the most patient benefit if
solved, and so on.
I think what would be really interesting as a research question, is whether deep learning
could pick up features on the OCT scan that we as humans are oblivious to, even with
specialist training. I know that deep learning has been applied to things like breast cancer
histopathology, and was able to pick up new features on the microscope slide that
correlate with five-year survival of patients with breast cancer. To do something similar
would be the Holy Grail in terms of OCT retinal imaging research.
https://theophthalmologist.com/issues/0716/on-a-quest-to-find-the-holy-grail-of-imaging/

RETINAL AI Companies Behold.ai
http://behold.ai/
medium.com/aws-activate-startuphttps://blogs.nvidia.com/blog/2016/02/09/deep-learning-3/

RETINAL AI Companies Orobix
http://www.orobix.com/
Copyright Daniele Cortinovis, Orobix Srl
https://github.com/orobix/retina-unet
Retina blood vessel segmentation with a
convolution neural network (U-net)
http://vmtklab.orobix.com/
Life science work focused on vessel segmentation and 3D
modeling of it, rather than purely on retinal pathologies.

Academic Approaches
S6516 - Automatic Gradingof Eye Diseases throughDeep Learning
Apaar Sadhwani Ph.D. Candidate, Stanford University
We'll outline the development of state-of-the-art medical imaging
system using novel deep architectures that harness GPUs for
accelerated training. Trained using data from Stanford Byers Eye
Institute and Palo Alto VA Hospital, our model grades the severity of
eye diseases and localizes lesions to help screen eye patients at primary care.
At the heart of this system lies our hybrid approach to deep learning for
high resolution images -- a large convnet with millions of parameters
trained with downsized images, fused with a net trained on selected
tiles of the high-resolution image. This innovative approach involves use
of transfer learning, data augmentation, and multi-GPU systems to
identify small-scale features that are critical to detecting eye diseases.
Tags: Deep Learning & Artificial Intelligence; Medical Imaging;
Supercomputing & HPC; Press-Suggested Sessions: AI & Deep Learning
http://arxiv.org/abs/1608.01339
https://arxiv.org/abs/1603.04833

Retinal Alzheimer Prediction #1
forbes.com
http://www.cognoptix.com/
http://dx.doi.org/10.4172/2161-0460.1000223
PhD Project: Multi-spectral imaging for in vivo imaging of
oxygen tension and -amyloidβ
The aim of this project is to build and clinically test a reliable multi-spectral imaging
device, that allows in vivo imaging of oxygen tension and -amyloid in human eyes.β
Maps showing the possible existence and distribution of -amyloid plaques will beβ
obtained in glaucoma patients and possibly patients with (early) Alzheimers’s
disease. A second goal is to develop software for hyperspectral image analysis for
early detection and diagnosis of these diseases, based on existing models
developed in our laboratory (Berendschot et al., 2010; van de Kraats et al., 1996).
Applicants
Dr. Tos TJM Berendschot
Prof. dr. Carroll AB Webers
University Eye Clinic Maastricht

Retinal Alzheimer Prediction #2 NeuroVision
http://www.austinblanco.com/nvi/
Diagnostics player NeuroVision raised $10 million in a Series B round, a
portion of which was reserved for strategic investors.
Wildcat Capital Management led the round with $5 million and Leonard
Potter, the firm's chief investment officer and president, will join
NeuroVision's board of directors.
https://www.crunchbase.com/organization/neurovision-imaging#/entity

Retinal Parkinson's Disease prediction
http://dx.doi.org/10.1186/s40478-016-0346-z
The occurrence of ocular manifestations in many neurodegenerative
diseases, including PD, emphasizes the strong connection between the
brain and the retina. Indeed, PD patients often suffer from visual
symptoms such as reduced visual acuity, low contrast sensitivity and
disturbed colour vision [9, 10]. In particular, recent findings have
highlighted the retina as a potential biomarker for PD. Thinning of the
inner retinal layer has shown to be an early event in patients with early
PD, where early disease was defined as diagnosis within 2.5 years with
an average disease stage of 2 (Hoehn–Yahr scale) [11, 12]. The
severity of PD symptoms correlates with RNFL thickness [13, 14].
Functional changes in the retina have also been recorded in early PD
(grade 1–1.5 Hoehn–Yahr scale, diagnosed within 3 years) [15] with
some suggestion that RGCs are involved early [16].
The aim of this study is to determine, using a rotenone-induced rodent
model of PD, whether firstly, retinal imaging can identify quantifiable
changes including retinal layer thickness (OCT) and RGC apoptotic
counts (DARC) in the natural history of the disease; and secondly, if
these changes can be used as surrogate biomarkers, and applied to
assessing a potential PD treatment strategy.
A modified Heidelberg HRA-OCT Spectralis (Heidelberg Engineering) was used for OCT imaging
[33]. A posterior pole scanning protocol, centred on the rat optic nerve head, was obtained in
each treatment group with TruTrack® software (Heidelberg Engineering), to ensure pixel to pixel
correspondence over time. Mean retinal thickness was determined using the HEYEX® thickness
map analysis (Heidelberg Engineering).
Affiliated with: UCL Institute of Ophthalmology, University College London
http://dx.doi.org/10.1371/journal.pone.0085718
http://dx.doi.org/10.1007%2Fs10633-015-9503-0

Other retinal markers for disease
Bipolar disorder
http://dx.doi.org/10.1016/j.comppsych.2016.02.005
Major depressive disorder
http://dx.doi.org/10.1016/j.comppsych.2016.02.005
Neuromyelitis optica and multiple sclerosis
http://dx.doi.org/10.1016/j.psychres.2015.07.028
Schizophrenia
http://dx.doi.org/10.1016/j.pscychresns.2011.08.011
Neurology in general
http://dx.doi.org/10.1097/WCO.0b013e328334e99b
Neurology in general #2
http://www.ncbi.nlm.nih.gov/pubmed/20065921
Huntington's disease
http://dx.doi.org/10.1002/mds.26486
Optical Coherence Tomography and Visual Field Findings in Patients With
Friedreich Ataxia
http://dx.doi.org/10.1097/WNO.0000000000000068
Retinal nerve fibre layer loss in hereditary spastic paraplegias is restricted
to complex phenotypes
http://dx.doi.org/10.1186/1471-2377-12-143
Retinal nerve fiber layer thickness in patients with essential tremor.
Retinal markers in various neurodegenerative disease
retinalphysician.com
http://dx.doi.org/10.1016/S1474-4422(16)00068-5

Regulatory affairs and patents

Regulatory affairs FDA Status of AI
dispatch.com
itnonline.com
Simply put, the FDA wants to keep computers on the periphery of diagnosis – and
even then is not comfortable with the use of diagnostic software. The agency has
cautiously defended the right of physicians – and only physicians – to practice
medicine. The FDAabsolutely will not step over that line, which pretty much negates
the chance that deep learning algorithms with even modest autonomy will pass FDA
review.
http://www.cio.com/article/3024715/government/luddites-clobber-ai-says-advocate.html
How will FDA regulate predictive clinical software as predictive analytics
pushes medical frontiers?
FDA & FTC Law, Healthcare Law
michaelhcohen.com/2015/06
Predictive analytics will push the frontiers of clinical care; the question is whether
FDA regulation will promote or stifle innovation in the name of consumer
protection. FDA regulation in this area is a moving target. Let’s see what we know
so far.
Fortune: Carl, why did you decide to move from real estate into healthcare and has it panned out
like you thought it would?
Carl Berg: I have been in the venture capital business for 40 years but I never once touched biotech
because I was concerned about the risk associated with government approval – it’s bad enough when
you’re doing venture capital but adding one more equation, like getting approval from the FDA [Food and
Drug Administration] makes it a lot harder. But about eight years ago I said, instead of getting into a whole
bunch of small companies, I am in a position now where I can do something really big in a hope that it
changes the world. So that’s what motivated me, and then I met with Niven, and that’s what got it started.
http://fortune.com/2015/04/16/cancer-cure-artificial-intelligence/

Patents for AI technology
One signal of this change is the dramatic increase in the number of companies making serious investments in Machine Learning. IBM is the
most famous example with Watson and their innovative cloud service. But Google, Amazon and Microsoft are also deeply involved. Even
Uber just bought a whole department of strong University talent from Carnegie-Mellon. These actions are not those of crazed corporate
lemmings pouring their money off the cliff. No, not at all. The investment is increasing because finally, after decades of disappointment, Machine
Learning algorithms are solving useful problems and delivering useful results.
Patent law was designed to protect the work of inventors. Inventors are the
ones “who conceived the invention.” Patents give inventors an exclusionary
right to their particular invention. Patent protection provides a mechanism to
compensate inventors for their investment in developing the invention, e.g., by
permitting others to practice their invention for a fee.
But the rising capability and prevalence of AI seem to pose a threat to this
system. First, as discussed before, identifying the problem itself is not
patentable. In some jurisdictions, problem identification by the inventor does
not protect against a claim of obviousness when the solution combines prior
known elements.
Consider then the case where the researcher presents the AI entity with a
problem to solve. The AI entity then crafts (or “deep learns”) the solution. Who
is the inventor of that solution? Certainly not the researcher, since current
law does not credit problem identification by itself worthy of patent protection.
Furthermore, the researcher didn’t actually create or even discover the
solution (the AI did).
The arrival of IBM’s Watson and his cousins from other companies is a game
changer. The new technology affects many things including the world of
Intellectual Property. Those who hate patents anyway will likely cheer this coming
world.
With some predicting that artificial intelligence (AI) will allow a patent to be filed
and granted without human intervention within the next 25 years, WIPR
assesses the potential impact of AI on the IP landscape.
worldipreview.com
cipa.org.uk/policy-and-news

AI in Ophthalmology | Startup Landscape

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