4. Digital Pathology
• Digital pathology is a branch of pathology that focuses on data
management based on -
Information generated from digitized glass slide
specimen converted from glass slides
8. • In pathology we do not need to feel texture of
the slides to make the diagnosis.
- Thus, apparently, digitalization in
pathology sounds more meaningful.
9. • Whole-slide imaging allows entire
slides to be imaged and
permanently stored at high
resolution.
- Telepathology.
- Solves physical storage
problem.
10. Advantage of digitalization in Pathology
include:
Assistance in primary diagnosis
Telepathology
Peer review
Intraoperative diagnosis
Training
Improved accuracy and speed
Immunohistochemistry (IHC) and can have a pivotal role in
clinical research
11. Global digital pathology market
• 2012: 1.98 billion dollar
• 2020: 5.7 billion dollar (Estimation)
12. • Expensive equipment, instable technology, lack of standards
and lab information.
• Personalized medicine and "big data" will remain nothing but
Buzz words.
• Dependencies.
• Even in the digital future the pathologist has to be able to dust
off and use the microscope as analogue assessment tool.
13. Risks
Quality as good or
better as light
microscopy?
Differences in human
interactions.
Misdiagnosis.
16. Digital pathology in Nepal
Pros:
• Can reduce laboratory expenses, improve operational efficacy,
enhanced productivity, and improve treatment decision and
patient care.
Cons:
• The high cost of scanners and AI-based software.
17. Future of digital pathology in Nepal
???
• Digital pathology could have much more
significant improvement in the developing
countries than in the developed ones.
18. Future???
When digital pathology is pathology
Digital pathology is on the verge of becoming a mainstream
option for routine diagnostics
“ In a time of drastic change it is the learners who inherit
the future. The learned usually find themselves equipped
to live in a world that no longer exists”
19. Things to be considered
RISK VS BENEFITS
LIMITATIONS
STANDARDIZATION
21. References
1. Abels, E. et al. (2019) ‘Computational pathology definitions, best practices, and
recommendations for regulatory guidance: a white paper from the Digital Pathology
Association’, Journal of Pathology, 249(3), pp. 286–294. doi: 10.1002/path.5331.
2. Fontelo, P. et al. (2012) ‘Digital pathology-implementation challenges in low-resource
countries’, Analytical Cellular Pathology, 35(1), pp. 31–36. doi: 10.3233/ACP-2011-
0024.
3. Jahn, S. W., Plass, M. and Moinfar, F. (2020) ‘Digital Pathology: Advantages,
Limitations and Emerging Perspectives’, Journal of Clinical Medicine, 9(11), p. 3697.
doi: 10.3390/jcm9113697.
4. Moxley-Wyles, B., Colling, R. and Verrill, C. (2020) ‘Artificial intelligence in
pathology: an overview’, Diagnostic Histopathology. Elsevier Ltd, 26(11), pp. 513–520.
doi: 10.1016/j.mpdhp.2020.08.004.
5. Niazi, M. K. K., Parwani, A. V. and Gurcan, M. N. (2019) ‘Digital pathology and
artificial intelligence’, The Lancet Oncology. Elsevier Ltd, 20(5), pp. e253–e261. doi:
10.1016/S1470-2045(19)30154-8.
6. Betigeri, A. M., Aparna, P. and Pasupathi, P. (2010) ‘Could India become the digital
pathology hub of the future?-A consideration of the prospects of telepathology
outsourcing Digital Pathology View project Could India become the digital pathology
hub of the future? A consideration of the prospects of telepa’, International Journal of
Biological & Medical Research Int J Biol Med Res, 1(4), pp. 300–302. Available at:
www.biomedscidirect.com.
Notas do Editor
Unlike other medical fields where tactile sensation could be an important part of examination,
Artificial intelligence in the primary diagnosis, diagnostic consultation.
Telepathology (a practice of sharing images at a distance).
Second opinion peer review.
Intraoperative diagnosis .
Medical student and resident training.
Deep learning and AI can improve accuracy and speed, increase diagnostic precision and will reduce intrasubjective variation.
Digital pathology can also help in immunohistochemistry (IHC) and can have a pivotal role in clinical research.
Artificial intelligence in the primary diagnosis, diagnostic consultation.
Telepathology (a practice of sharing images at a distance).
Second opinion peer review.
Intraoperative diagnosis .
Medical student and resident training.
Deep learning and AI can improve accuracy and speed, increase diagnostic precision and will reduce intrasubjective variation.
Digital pathology can also help in immunohistochemistry (IHC) and can have a pivotal role in clinical research.
Personalized medicine and "big data" will remain nothing but buzz words if the information systems don't manage to meet the requirements of digitalisation. At the same time we should not forget that the new possibilities will also create new dependencies. They need to be addressed in order to avoid a complete breakdown of the diagnostic tasks that are performed every single day. Thus, any new pathology LIS solution needs to offer a reliable disaster recovery strategy, including the post-failure input of analogue data. Even in the digital future the pathologist has to be able to dust off and use the microscope as analogue assessment tool. In short: digitalisation supports the pathologist's assessment - it does not replace it!
As in many arguments, both sides may be correct. The value of digital pathology today is likely greatly overstated, stemming from an approach that uses brute force to solve the problems of subjectivity and low reproducibility.
Alternatively, digital pathology can bring cost savings, improvements in care and enhanced levels of lab efficiency, yet likely only when done in a manner that takes into account the unique workflow and characteristics of pathology.
When understanding why pathology is different from other diagnostic sciences, it becomes clear how correct the 90/10 rule is and where the disadvantages lay. And at the same time, how digital pathology can play an important role in improving workflow and standard of care.
As in many arguments, both sides may be correct. The value of digital pathology today is likely greatly overstated, stemming from an approach that uses brute force to solve the problems of subjectivity and low reproducibility.
Alternatively, digital pathology can bring cost savings, improvements in care and enhanced levels of lab efficiency, yet likely only when done in a manner that takes into account the unique workflow and characteristics of pathology.
When understanding why pathology is different from other diagnostic sciences, it becomes clear how correct the 90/10 rule is and where the disadvantages lay. And at the same time, how digital pathology can play an important role in improving workflow and standard of care.
since they have already much robust health care system.