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Scoring metrics for assessing skills in arthroscopic rotator cuff repair: performance comparison study of novice and expert surgeons

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

Abstract

Purpose

We aim to develop quantitative performance metrics and a deep learning model to objectively assess surgery skills between the novice and the expert surgeons for arthroscopic rotator cuff surgery. These proposed metrics can be used to give the surgeon an objective and a quantitative self-assessment platform.

Methods

Ten shoulder arthroscopic rotator cuff surgeries were performed by two novices, and fourteen were performed by two expert surgeons. These surgeries were statistically analyzed. Two existing evaluation systems: Basic Arthroscopic Knee Skill Scoring System (BAKSSS) and the Arthroscopic Surgical Skill Evaluation Tool (ASSET), were used to validate our proposed metrics. In addition, a deep learning-based model called Automated Arthroscopic Video Evaluation Tool (AAVET) was developed toward automating quantitative assessments.

Results

The results revealed that novice surgeons used surgical tools approximately 10% less effectively and identified and stopped bleeding less swiftly. Our results showed a notable difference in the performance score between the experts and novices, and our metrics successfully identified these at the task level. Moreover, the F1-scores of each class are found as 78%, 87%, and 77% for classifying cases with no-tool, electrocautery, and shaver tool, respectively.

Conclusion

We have constructed quantitative metrics that identified differences in the performances of expert and novice surgeons. Our ultimate goal is to validate metrics further and incorporate these into our virtual rotator cuff surgery simulator (ViRCAST), which has been under development. The initial results from AAVET show that the capability of the toolbox can be extended to create a fully automated performance evaluation platform.

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Acknowledgements

The authors would like to thank Seth Cooper-Baer and Mustafa Tunc for their contributions to this publication. This publication was made possible by the Grant NIH/NIAMS R44AR075481-01. This project was also supported by the Arkansas INBRE program (NIGMS, P20 GM103429), NIH/NCI 5R01CA197491, and NIH/NHLBI NIH/NIBIB 1R01EB025241, R56EB026490.

Funding

This project was made possible by the Arkansas INBRE program, supported by a grant from the National Institute of General Medical Sciences (NIGMS), P20 GM103429 from the National Institutes of Health (NIH). This project was also supported by NIH/NIAMS R44AR075481-01, NIH/NCI 5R01CA197491, and NIH/NHLBI NIH/NIBIB 1R01EB025241, R56EB026490.

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Authors and Affiliations

  1. Computer Science Department, Florida Polytechnic University, 4700 Research Way, Lakeland, FL, 33805, USA

    Doga Demirel, Bryce Palmer, Gunnar Sundberg & Bayazit Karaman

  2. Computer Science Department, University of Central Arkansas, Conway, AR, USA

    Tansel Halic & Sinan Kockara

  3. Department of Orthopedics and Traumatology, Erzincan University Medical School, Erzincan, Turkey

    Nizamettin Kockara

  4. Alabama Ortho Spine and Sports in Birmingham, Birmingham, AL, USA

    Mark Edward Rogers

  5. Memorial Orthopaedic Surgical Group, Long Beach, CA, USA

    Shahryar Ahmadi

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  1. Doga Demirel
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Correspondence to Doga Demirel.

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Demirel, D., Palmer, B., Sundberg, G. et al. Scoring metrics for assessing skills in arthroscopic rotator cuff repair: performance comparison study of novice and expert surgeons. Int J CARS 17, 1823–1835 (2022). https://doi.org/10.1007/s11548-022-02683-3

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  • DOI: https://doi.org/10.1007/s11548-022-02683-3

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