Deep Learning to Automate Technical Skills Assessment in Robotic Surgery

Abstract

Surgeon performance affects patient outcomes. To improve patient outcomes, we must identify poor surgical performance. However, surgeons may not always associate a specific surgical act with its consequential outcome unless the error is egregious and the outcome is immediate. Today, there is little formal structure for surgeons to receive specific technical skills feedback after formal training. Current hurdles for surgeons to obtain and maintain hospital privileges to perform an operative procedure include peer proctoring and evaluation, which are arguably insufficient when juxtaposed to the potentially devastating outcomes that can occur if surgical errors arise.