Lifelong Learning for Therapeutic Endoscopy

PI: Daniel Hashimoto (PSOM), Gregory Ginsberg (GI), Eric Eaton (SEAS)

Funding: American Society for Gastrointestinal Endoscopy

While different deep learning approaches have been successful in identifying phases of surgery or instruments, existing approaches are limited to individual operations. However, many procedures share steps or are performed on the same organ(s). Such interconnections offer the potential for improved clinical benefit by incorporating recent advances in lifelong deep learning, which enables transfer of knowledge across procedures and continual improvement over time. This project focuses on investigating knowledge transfer across “third space” therapeutic endoscopic procedures.


Metrics Revolutions: Clinically Relevant Outcome Measures for Computer Vision in Surgery

PI: Daniel Hashimoto, Lena Maier-Hein (Univ of Heidelberg), Amin Madani (UHN), Stefanie Speidel (Dresden), Dan Stoyanov (UCL)

Building off the Metrics Reloaded project to provide guidance to researchers on appropriate selection of metrics to evaluate the performance of visual models in biomedical imaging tasks, the Metrics Revolutions project seeks to outline more specific, clinically oriented metrics to assess the performance of models that work with visual surgical data.


Patient Perceptions of Therapeutic AI

PI: Daniel Hashimoto

         While much of the effort in applying AI to surgery has focused on developing methods for annotating ground truth in surgery and building visual models for the analysis of surgical video, few efforts have sought to determine how patients feel about the possibility of AI influencing the decisions that their physicians may make during a therapeutic procedure such as surgery. This work surveys the landscape of existing literature and seeks the input of a wide range of patients to better understand how patients understand and perceive the role of AI in their care.