Capturing fine-grained details for video-based automation of suturing skills assessment.

Journal: International journal of computer assisted radiology and surgery
PMID: 36282465

Abstract

OBJECTIVES: Manually-collected suturing technical skill scores are strong predictors of continence recovery after robotic radical prostatectomy. Herein, we automate suturing technical skill scoring through computer vision (CV) methods as a scalable method to provide feedback.

Authors

  • Andrew J Hung
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California. Electronic address: Andrew.Hung@med.usc.edu.
  • Richard Bao
    Department of Computing & Mathematical Sciences, Caltech, Pasadena, CA, USA.
  • Idris O Sunmola
    McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
  • De-An Huang
    NVIDIA, Santa Clara, CA, USA.
  • Jessica H Nguyen
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA.
  • Anima Anandkumar
    Department of Computing and Mathematical Science, California Institute of Technology, Pasadena, California; NVIDIA Corporation, Santa Clara, California.

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