Ph.D. Dissertation Defense - Aneeq Zia

Event Details

Wednesday, October 31, 2018

1:00pm - 3:00pm

Room 102A, MiRC

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Event Details

TitleAutomated Benchmarking of Surgical Skills using Machine Learning


Dr. Irfan Essa, CoC, Chair , Advisor

Dr. Patricio Vela, ECE

Dr. Thomas Ploetz, IC

Dr. David Anderson, ECE

Dr. Anthony Jarc, Intuitive Surgical


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.

Last revised October 19, 2018