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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Automated Benchmarking of Surgical Skills using Machine Learning
Committee:
Dr. Irfan Essa, CoC, Chair , Advisor
Dr. Patricio Vela, ECE
Dr. Thomas Ploetz, IC
Dr. David Anderson, ECE
Dr. Anthony Jarc, Intuitive Surgical
Abstract:
Surgical trainees are required to acquire specific skills during the course of their residency before performing real surgeries. Surgical training involves constant practice of skills and seeking feedback from supervising surgeons, who generally have a packed schedule. The process of manual assessment makes the whole training cycle extremely cumbersome and inefficient. Having automated assessment systems for surgical training can be of great value to medical schools and teaching hospitals. The aim of this PhD research is to develop machine learning based methods for assessment of surgical skills from basic tasks to complex robot-assisted procedures. Specifically, this thesis will aim to (1) develop novel motion based features for basic surgical skills assessment in open and robotic surgical training, (2) develop unsupervised and supervised methods for recognizing individual steps of complex robot-assisted (RA) surgical procedures, (3) generate automated score reports for RA surgical procedures, and (4) produce video highlights to indicate which parts of the surgical task most effected the final surgical skill score.
