Interoperative Performance Measurement of Surgeons using Deep Learning

Surgery is undoubtedly one of the most important events in a person’s life. It is thus imperative that a feedback system is in place to ensure that proper care is provided to patients during surgery. Currently, such systems involve experienced surgeons watching hours of surgery to determine how well the surgery was performed based on pre-defined criteria. This project aims to assign the surgeon’s performance rating based on data from their previous procedures. In doing so, surgeons will be assigned a technical competence rating based on their performance. A consistent rating will help differentiate surgeons that are very skilled at their craft and ones that require more training. It will also provide surgeons with additional incentive to hone their skills thereby increasing positive outcomes for their patients.

Faculty Supervisor:

Sanja Fidler


Shuja Khalid


Surgical Safety Technologies Inc


Computer science


Medical devices




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