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Modeling Performance of Open Surgical Cases. | LitMetric

Modeling Performance of Open Surgical Cases.

Simul Healthc

From the Department of Industrial and Systems Engineering (D.P.A., R.G.R.); Department of Surgery (S.R.P.Q., C.C.G.), Clinical Sciences Center; Department of Urology (B.V.L.); and Duane H. and Dorothy M. Bluemke Professor in the College of Engineering (R.G.R.), University of Wisconsin-Madison, Madison, WI; Department of Surgery (L.L.F.), Penn Medicine - University of Pennsylvania Health System, Philadelphia, PA; City of Hope National Comprehensive Cancer Center (B.L.M), Duarte, CA.

Published: December 2021

AI Article Synopsis

  • This study evaluates how well computer-generated assessments of surgical hand motion match expert reviews for suturing and tying tasks recorded in an operating room (OR) setting.
  • The research indicates that while computer assessments for fluidity of motion perform better than those for motion economy, both struggle to match the accuracy of expert ratings, especially in the dynamic OR environment.
  • The findings suggest that though computer-generated assessments could offer useful feedback during practice, their effectiveness varies significantly compared to controlled simulation environments, highlighting a need for more extensive video data for future studies.

Article Abstract

Introduction: Previous efforts used digital video to develop computer-generated assessments of surgical hand motion economy and fluidity of motion. This study tests how well previously trained assessment models match expert ratings of suturing and tying video clips recorded in a new operating room (OR) setting.

Methods: Enabled through computer vision of the hands, this study tests the applicability of assessments born out of benchtop simulations to in vivo suturing and tying tasks recorded in the OR.

Results: Compared with expert ratings, computer-generated assessments for fluidity of motion (slope = 0.83, intercept = 1.77, R2 = 0.55) performed better than motion economy (slope = 0.73, intercept = 2.04, R2 = 0.49), although 85% of ratings for both models were within ±2 of the expert response. Neither assessment performed as well in the OR as they did on the training data. Assessments were sensitive to changing hand postures, dropped ligatures, and poor tissue contact-features typically missing from training data. Computer-generated assessment of OR tasks was contingent on a clear, consistent view of both surgeon's hands.

Conclusions: Computer-generated assessment may help provide formative feedback during deliberate practice, albeit with greater variability in the OR compared with benchtop simulations. Future work will benefit from expanded available bimanual video records.

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Source
http://dx.doi.org/10.1097/SIH.0000000000000544DOI Listing

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