In this presentation we will make an attempt to answer the question of how far AI from revolutionising healthcare and what is the current progress in this area. I have looked into the latest groundbreaking medical innovations driven by deep learning and evaluate their potential impact on medical practice. I have discussed the main challenges that deep learning engineers face and recent advances that have been proposed in deep learning in order to address these challenges. Most of the presentation is based on the recently accepted review paper – Computational biology – deep learning by William Jones, Kaur Alasoo, Dmytro Fishman et al.
38. Diabetic Retinopathy
Development and Validation of a Deep
Learning Algorithm for Detection of Diabetic
Retinopathy in Retinal Fundus Photographs
Dermatologist-level classification of skin
cancer with deep neural networks
Skin Cancer
39. Development and Validation of a Deep
Learning Algorithm for Detection of Diabetic
Retinopathy in Retinal Fundus Photographs
Dermatologist-level classification of skin
cancer with deep neural networks
Skin Cancer
Diabetic Retinopathy
58. Algorithm vs Ophthalmologists
Sensitivity,%
100 - Specificity, %
0100
0 100
AUC of 97.4%
Points on ROC are
performances of
individual
ophthalmologists
The black curve is ROC
for the Deep Learning
algorithm
59. Performances are very
similar
Algorithm vs Ophthalmologists
Points on ROC are
performances of
individual
ophthalmologists
The black curve is ROC
for the Deep Learning
algorithm
Sensitivity,%
100 - Specificity, %
0100
0 100
AUC of 97.4%
60. Deep Learning algorithm
can operate in any point
on the curve
Algorithm vs Ophthalmologists
Sensitivity,%
100 - Specificity, %
0100
0 100
AUC of 97.4%
61. Deep Learning algorithm
can operate in any point
on the curve
Sensitivity,%
100 - Specificity, %
0100
0 100
AUC of 97.4%
Algorithm vs Ophthalmologists
High specificity mode (diagnosis)
62. Deep Learning algorithm
can operate in any point
on the curve
Sensitivity,%
100 - Specificity, %
0100
0 100
AUC of 97.4%
Algorithm vs Ophthalmologists
High specificity mode (diagnosis)
High sensitivity mode (screening)
63. Deep Learning algorithm
can operate in any point
on the curve
Sensitivity,%
100 - Specificity, %
0100
0 100
AUC of 97.4%
Algorithm vs Ophthalmologists
High specificity mode (diagnosis)
High sensitivity mode (screening)
While
ophthalmologists’s
mode is fixed by
experience
64. Diabetic Retinopathy
Development and Validation of a Deep
Learning Algorithm for Detection of Diabetic
Retinopathy in Retinal Fundus Photographs
Dermatologist-level classification of skin
cancer with deep neural networks
Skin Cancer
65. Development and Validation of a Deep
Learning Algorithm for Detection of Diabetic
Retinopathy in Retinal Fundus Photographs
Dermatologist-level classification of skin
cancer with deep neural networks
Diabetic Retinopathy
Skin Cancer
96. Each of the cases
was verified by
biopsy
Specificity,%
Sensitivity, %
AUC of 96%
Carcinoma:
135 images
Dermatologists (25)
Algorithm vs Dermatologists
98. Specificity,%
Sensitivity, %
AUC of 96%
Carcinoma:
135 images
Dermatologists (25)
Algorithm vs Dermatologists
Performance of the
algorithm was compared
to dermatologists
99. Performance of the
algorithm was compared
to dermatologists
Average dermatologist’s
performance was marked
as
Specificity,%
Sensitivity, %
AUC of 96%
Carcinoma:
135 images
Dermatologists (25)
Algorithm vs Dermatologists
100. Performance of the
algorithm was compared
to dermatologists
Average dermatologist’s
performance was marked
as
Specificity,%
Sensitivity, %
AUC of 96%
Carcinoma:
135 images
Dermatologists (25)
Algorithm vs Dermatologists
101. Specificity,%
Sensitivity, %
AUC of 96%
Specificity,%
Sensitivity, %
AUC of 94%
Specificity,%
Sensitivity, %
AUC of 91%
Carcinoma:
135 images
Melanoma:
130 images
Melanoma:
111 images
Dermatologists (25) Dermatologists (22) Dermatologists (21)
Algorithm vs Dermatologists
102. Specificity,%
Sensitivity, %
AUC of 96%
Specificity,%
Sensitivity, %
AUC of 94%
Specificity,%
Sensitivity, %
AUC of 91%
Carcinoma:
135 images
Melanoma:
130 images
Melanoma:
111 images
Dermatologists (25) Dermatologists (22) Dermatologists (21)
Algorithm vs Dermatologists
Across all biopsy verified datasets Deep Neural
Network was superior
103. Diabetic Retinopathy
Development and Validation of a Deep
Learning Algorithm for Detection of Diabetic
Retinopathy in Retinal Fundus Photographs
Dermatologist-level classification of skin
cancer with deep neural networks
Skin Cancer
https://jamanetwork.com/journals/jama/fullarticle/2588763
https://www.nature.com/nature/journal/v542/n7639/full/
nature21056.html
105. Diagnosing Parkinson from voice
(Al-Fatlawi et al., 2016)
Detection of hypoglycemic
episodes in children (San et al.,
2016)
HemoglobinA1c
03.2010
Timeline
07.2010
12.2010
02.2011
04.2011
?
Painintensity
Frames
Pain estimation from video
(Zhou et al., 2016)
Predicting subsequent
hospitalisation (Choi et al., 2016)
113. Chart of possible reasons why deep
learning may fail to revolutionise medicineLikelihood
Effect
UnlikelyHighlylikely
Not nice, but ok Terrible consequences
120. We can build a model that
can distinguish them from
other objects
We may fail to compose large
enough datasets
121. We can build a model that
can distinguish them from
other objects
We may fail to compose large
enough datasets
122. We can build a model that
can distinguish them from
other objects
We cannot build a robust
representation for all of them
We may fail to compose large
enough datasets
123. We can build a model that
can distinguish them from
other objects
We would need a separate
ImageNet for each type
We may fail to compose large
enough datasets
We cannot build a robust
representation for all of them
126. Chart of possible reasons why deep
learning may fail to revolutionise medicineLikelihood
Effect
UnlikelyHighlylikely
Not nice, but ok Terrible consequences
Data
128. There is a ABCD rule
they learned in college
How doctors diagnose
melanomas?
129. There is a ABCD rule
they learned in college
Melanomas are
Asymmetrical
How doctors diagnose
melanomas?
130. There is a ABCD rule
they learned in college
Melanomas are
Asymmetrical
How doctors diagnose
melanomas?
131. There is a ABCD rule
they learned in college
Melanomas are
Asymmetrical
How doctors diagnose
melanomas?
Their Borders
are uneven
132. There is a ABCD rule
they learned in college
Melanomas are
Asymmetrical
Their Borders
are uneven
How doctors diagnose
melanomas?
133. There is a ABCD rule
they learned in college
Melanomas are
Asymmetrical
Colour can be
patchy and
variegated
How doctors diagnose
melanomas?
Their Borders
are uneven
134. There is a ABCD rule
they learned in college
Melanomas are
Asymmetrical
Colour can be
patchy and
variegated
Their Borders
are uneven
How doctors diagnose
melanomas?
135. There is a ABCD rule
they learned in college
Melanomas are
Asymmetrical
Colour can be
patchy and
variegated
How doctors diagnose
melanomas?
Their Borders
are uneven
136. There is a ABCD rule
they learned in college
Colour can be
patchy and
variegated
their Diameter is
usually > 6
millimetres
How doctors diagnose
melanomas?
Melanomas are
Asymmetrical
Their Borders
are uneven
137. There is a ABCD rule
they learned in college
Melanomas are
Asymmetrical
Their Borders
are uneven
Colour can be
patchy and
variegated
their Diameter is
usually > 6
millimetres
How doctors diagnose
melanomas?
143. Chart of possible reasons why deep
learning may fail to revolutionise medicineLikelihood
Effect
UnlikelyHighlylikely
Not nice, but ok Terrible consequences
DataInterpretability
177. Turning Machine Intelligence
Against Lung Cancer
http://www.datasciencebowl.com/competitions/turning-machine-intelligence-against-lung-cance
Team: Lauri Listak
Supervisor: Dmytro Fishman
178. Turning Machine Intelligence
Against Lung Cancer
http://www.datasciencebowl.com/competitions/turning-machine-intelligence-against-lung-cance
Team: Lauri Listak
Supervisor: Dmytro Fishman
20%
of lung cancer deaths
can be reduced with
early detection
179. High False
Positives rates
lead to interventional
treatments, additional
costs and patient
anxiety
20%
of lung cancer deaths
can be reduced with
early detection
Turning Machine Intelligence
Against Lung Cancer
http://www.datasciencebowl.com/competitions/turning-machine-intelligence-against-lung-cance
Team: Lauri Listak
Supervisor: Dmytro Fishman
187. References
• Series of blog posts “Do machines actually beat doctors?” by
Luke Oakden-Rayner (https://lukeoakdenrayner.wordpress.com/
2016/11/27/do-computers-already-outperform-doctors/)
• Opportunities and obstacles for deep learning in biology and
medicine by Ching et al. (http://www.biorxiv.org/content/biorxiv/
early/2017/05/28/142760.full.pdf)
• Computational biology - deep learning by William Jones, Kaur
Alasoo, Dmytro Fishman et al. (accepted)