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Multi-modal Deep Learning for Assessing Surgeon Technical Skill on a Surgical Knot-tying Task
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  • Kevin Kasa ,
  • David Burns ,
  • Mitchell Goldenberg ,
  • Omar Selim ,
  • Cari Whyne ,
  • Michael Hardisty
Kevin Kasa
Sunnybrook Research Institute

Corresponding Author:[email protected]

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David Burns
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Mitchell Goldenberg
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Omar Selim
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Cari Whyne
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Michael Hardisty
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Abstract

This paper introduces a new dataset of a surgical knot-tying task, and a multi-modal deep learning model that achieves comparable performance to expert human raters on this skill  assessment task. Seventy-two surgical trainees and faculty were recruited for the knot-tying task, and were recorded using video, kinematic, and image data. Three expert human raters conducted the skills assessment using the Objective Structured Assessment of Technical Skill (OSATS) Global Rating Scale (GRS). We also designed and developed three deep learning models: a ResNet-based image model, a ResNet-LSTM kinematic model, and a multi-modal model leveraging the image and timeseries kinematic data. Results: All three models demonstrate performance comparable to the expert human raters on most GRS domains. The multi-modal model demonstrates the best overall performance, as measured using the mean squared error (MSE) and intraclass correlation coefficient (ICC). We found that multi-modal deep learning has the potential to replicate human raters on a challenging human-performed knot-tying task. As objective assessment of technical skill continues to be a growing, but resource-heavy, element of surgical education, this study is an important step towards automated surgical skill assessment, ultimately leading to reduced burden on training faculty and institutes.