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Surgical Innovation
September 15, 2021

Deep Learning to Automate Technical Skills Assessment in Robotic Surgery

Author Affiliations
  • 1Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology USC Institute of Urology, University of Southern California, Los Angeles
  • 2Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles
  • 3Department of Computing & Mathematical Sciences, Caltech, Pasadena, California
JAMA Surg. Published online September 15, 2021. doi:10.1001/jamasurg.2021.3651

Surgeon performance affects patient outcomes.1 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.

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