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Automatic performance evaluation of the intracorporeal suture exercise

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

This work uses deep learning algorithms to provide automated feedback on the suture with intracorporeal knot exercise in the fundamentals of laparoscopic surgery simulator. Different metrics were designed to provide informative feedback to the user on how to complete the task more efficiently. The automation of the feedback will allow students to practice at any time without the supervision of experts.

Methods

Five residents and five senior surgeons participated in the study. Object detection, image classification, and semantic segmentation deep learning algorithms were used to collect statistics on the practitioner’s performance. Three task-specific metrics were defined. The metrics refer to the way the practitioner holds the needle before the insertion to the Penrose drain, and the amount of movement of the Penrose drain during the needle’s insertion.

Results

Good agreement between the human labeling and the different algorithms’ performance and metric values was achieved. The difference between the scores of the senior surgeons and the surgical residents was statistically significant for one of the metrics.

Conclusion

We developed a system that provides performance metrics of the intracorporeal suture exercise. These metrics can help surgical residents practice independently and receive informative feedback on how they entered the needle into the Penrose.

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Funding

Study approval was granted by the Technion-Israel Institute of Technology Institutional Review Board. Informed consent was obtained from all individual participants included in the study. Funding was provided by the E. & J. Bishop Research Fund.

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Authors and Affiliations

  1. Faculty of Data and Decision Sciences, Technion – Israel Institute of Technology, 3200003, Haifa, Israel

    Liran Halperin, Ido Zuckerman & Shlomi Laufer

  2. Department of General Surgery, Bnai-Zion Medical Center, Haifa, Israel

    Gideon Sroka

Authors
  1. Liran Halperin
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  2. Gideon Sroka
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  3. Ido Zuckerman
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  4. Shlomi Laufer
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Corresponding author

Correspondence to Liran Halperin.

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The authors declare that they have no conflict of interest.

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Halperin, L., Sroka, G., Zuckerman, I. et al. Automatic performance evaluation of the intracorporeal suture exercise. Int J CARS 19, 83–86 (2024). https://doi.org/10.1007/s11548-023-02963-6

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  • DOI: https://doi.org/10.1007/s11548-023-02963-6

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