Pilot Analysis of Surgeon Instrument Utilization Signatures Based on Shannon Entropy and Deep Learning for Surgeon Performance Assessment in a Cadaveric Carotid Artery Injury Control Simulation.

Journal: Operative neurosurgery (Hagerstown, Md.)
PMID: 37655892

Abstract

BACKGROUND AND OBJECTIVES: Assessment and feedback are critical to surgical education, but direct observational feedback by experts is rarely provided because of time constraints and is typically only qualitative. Automated, video-based, quantitative feedback on surgical performance could address this gap, improving surgical training. The authors aim to demonstrate the ability of Shannon entropy (ShEn), an information theory metric that quantifies series diversity, to predict surgical performance using instrument detections generated through deep learning.

Authors

  • Alan Balu
    Department of Neurosurgery, Georgetown University School of Medicine, Washington , District of Columbia, USA.
  • Dhiraj J Pangal
    Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. Electronic address: pangal@usc.edu.
  • Guillaume Kugener
    Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
  • Daniel A Donoho
    Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA; Division of Neurosurgery, Department of Surgery, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA.

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