1. Models for Implementing Artificial Intelligence in Pathology Practice
May 6, 2019
Douglas J. Hartman, MD

2. Disclosures
Philips = honorarium for educational presentation

3. Objectives
Describe an evolving model for the diagnostic cockpit and how that applies to pathology
Demonstrate a sample diagnostic signout workflow
Describe how artificial intelligence can be integrated into the pathology diagnostic workflow

4. Pathology Cockpit Early discussions of a pathology cockpit in 2010
Very little literature about this exists
Diagn Pathol. 2014;9 Suppl 1:S12. doi: / S1-S12. Epub 2014 Dec 19.
iPathology cockpit diagnostic station: validation according to College of American Pathologists Pathology and Laboratory Quality Center recommendation at the Hospital Trust and University of Verona.
Brunelli M, Beccari S, Colombari R, Gobbo S, Giobelli L, Pellegrini A, Chilosi M, Lunardi M, Martignoni G, Scarpa A, Eccher A.

5. Cockpit Evolution
Forum sponsored by the Academy for Radiology and Biomedical Imaging Research
Brought together stakeholders from:
Urology
Oncology
Pathology
Neurology
Cardiology
Emergency Medicine
Molecular Diagnostics
Informatics

6. Goals of the Symposium
Elevate the profile of medical imaging technologies
Ensures its value and impact are broadly recognized
Facilitate collaborations
Highlight content experts
Provide critical voice of the imaging community

7. Drivers for convening symposium
Errors and imprecision in medical diagnosis
Leads to poor patient outcomes
Two common diagnostic errors
Ordering the wrong imaging test
Misinterpretation of imaging test findings

8. Cockpit = “Integrated Diagnostics”
Current Obstacles:
Limited dissemination of valuable diagnostic technologies
Siloed electronic health records
Few available data analytic tools
Variable data inputs and outputs
Lack of coordinated effort to improve diagnostics across all stakeholders

9. Symposium Composition
Presentations from key stakeholders (“consumers”)
Presentations from the “Cockpit Crew” (users)
Multidisciplinary groups to identify models and prioritize the next steps in constructing a prototype Cockpit

10. High Priority Tasks Develop national standards
Catalog available data analytic methods
Develop advanced analytic methods
Develop environment to host the analytic methods
Create an environment to enable and facilitate communication/cooperation
Catalyze the construction of the prototype Cockpit

11. Advancing the Diagnostic Cockpit
May 2018
Four objectives:
Standardization/interoperability
Application of advanced computation
Acceleration of development and translation of new techniques
Promotion of best practices in medical imaging

12. Challenges Lack of standardized measurement and techniques
Lack of standard interchange mechanism
Need for more standardized reference studies

13. Action Items from Symposium
Identify neutral third party to validate de-identification, manage datasets and ensure interoperability
Collect 100 complete datasets from 10 different institutions
Create compendium of existing standards
Refine core functional requirements of the diagnostic cockpit
Identify potential funding sources for any initiative

14. So what does this mean for pathology?

15. Digital Pathology Background
Very few labs in the USA have gone entirely digital for primary diagnosis
UPMC is restarting their conversion to a digital pathology platform for primary diagnosis sign out
Vendors have been regulated by Food Drug Administration – WSI considered Class III device
First FDA approved system – Philips – April 2017

16. UPMC Clinical Use Cases
Retrospective Slide Scanning (passive encouragement of slide scanning – archival use case)
Internal (UPMC) & external consults
IHC core lab (centralization)
Breast marker image analysis
Philips Tutor (formerly PathXL) education partnership
To do:
Archive outside consults, frozen section, tumor board, etc.

17. Precision Medicine

18. Law of Disruption

19. The Perfect Storm
“When technologies, products, and services converge in radical, creative new ways, a killer app can emerge”
Technology
Products
Services
Dones & Mui. Unleashing the killer app. Harvard Business School Press. 2000

20. Algorithms Identify rare events (e.g. screening for microorganisms)
Quantitative measurements
Score biomarkers (e.g. ER, PR, Her2/neu, Ki67, CD34, PD-L1)
Tissue measurements (e.g. mitotic counts, quantify fibrosis/steatosis)
Analyze spatial patterns and feature distribution (e.g. neuroscience)
Automated grading (of tumors)
CAD (e.g. prostate cancer diagnosis, detect Barrett’s esophagus with dysplasia)
Workflow (smart) algorithm (e.g. triage cases, automate downstream steps like LCM)
Miscellaneous (research & novel) algorithms (e.g. TMAs, 3D image reconstruction)

23. Integration in Digital Signout -1

24. Integration in Digital Signout -2

25. Integration in Digital Signout -3

26. Integration in Digital Signout - 4

27. Digital Workflow
Digital Workflow
Legacy Workflow

28. Integration in Digital Signout - 5

29. Integration in Digital Signout -6

30. Implemented Image Analysis
Automated CD8 quantification
Digital tumor bud assessment
Automated Her2 assessment in GE junction tumors
Developing more biomarker evaluation

31. CD8 Analysis Oropharyngeal Cases
?Selection criteria to use
Tonsillar tissue
Resection vs biopsy vs TMA core

32. Whole section Analysis

33. Absolute CD8 distribution within Oropharyngeal cases

34. Outcome Data based on Univariate Analysis (n=74)

35. Survival based on Density of CD8 cells

36. Digital Tumor Bud Assessment

37. Counting Ki-67 positive cells

38. Regulatory
Few AI-based algorithms have been cleared by the Food and Drug Administration
Use cases:
Bone fracture
Large vessel occlusion
Brain damage
Diabetic retinopathy

39. Payment
No billing code specifically associated with artificial intelligence
Options:
Per Click
Flat fee
Other?

41. Public Image Challenges
Image analysis challenges
Camelyon – 16 – detecting metastases
Cataloged at Grand Challenges
Public leaderboard of contestants

42. Summary of Pathology Image Challenges
19/169 challenges involved pathology
slides
Ranged from 10-40x magnification
The number of recorded participants ranged from

43. Summary of Pathology Image Challenges – organ area and file types
Organ sites:
Breast (9)
Cervix (2)
Neuropath (2)
Multiorgan (2)
Thyroid (1); Colorectal (1); lung (1); hemepath (1)
Most common to have a single file type (15 challenges)

44. Image Challenges - evaluation
Various statistical methods
Dice Coefficient
Area under the curve
Weighted precision
Free response operating curve
F1 score
Sensitivity/specificity
Gold standard
One pathologist
Multiple “consensus” pathologists
Medical Experts
Oncologist
Not mentioned

45. Future Directions
Cockpit for Pathology already forming (mostly around software system integration)
Most designs have tried to emulate current workflows
Novel interactions with whole slide images (Virtual reality/augmented reality)

46. Conclusions
Artificial Intelligence based tools are ready for use in the diagnostic pathology workflow
Building interoperability with digital pathology systems is critical to adoption
Integrating artificial intelligence into digital pathology platforms with increase adoption and facilitate more utilization of this powerful technology

47. Questions and Answers
Douglas J. Hartman MD

48. References
Mark D. Zarella, Douglas Bowman;, Famke Aeffner, Navid Farahani, Albert Xthona;, Syeda Fatima Absar, Anil Parwani, Marilyn Bui, and Douglas J. Hartman (2019) A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association. Archives of Pathology & Laboratory Medicine: February 2019, Vol. 143, No. 2, pp
Guo H, Birsa J, Farahani N, Hartman DJ, Piccoli A, O'Leary M, McHugh J, Nyman M, Stratman C, Kvarnstrom V, Yousem S, Pantanowitz L. Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out. J Pathol Inform May 4;7:23. doi: / eCollection PMID:
Ratner M. FDA backs clinician-free AI imaging diagnostic tools. Nat Biotechnol 2018 Aug 6; 36(8):