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Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks

Abstract

We seek to understand if an automated algorithm can replace human scoring of surgical trainees performing the urethrovesical anastomosis in radical prostatectomy with synthetic tissue. Specifically, we investigate neural networks for predicting the surgical proficiency score (GEARS score) from video clips. We evaluate videos of surgeons performing the urethral anastomosis using synthetic tissue. The algorithm tracks surgical instrument locations from video, saving the positions of key points on the instruments over time. These positional features are used to train a multi-task convolutional network to infer each sub-category of the GEARS score to determine the proficiency level of trainees. Experimental results demonstrate that the proposed method achieves good performance with scores matching manual inspection in 86.1% of all GEARS sub-categories. Furthermore, the model can detect the difference between proficiency (novice to expert) in 83.3% of videos. Evaluation of GEARS sub-categories with artificial neural networks is possible for novice and intermediate surgeons, but additional research is needed to understand if expert surgeons can be evaluated with a similar automated system.

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Availability of data and material

Videos available upon request, with proper data management plan and IRB reciprocity approval.

Code availability

All source code is custom developed and available upon request (https://github.smu.edu/48066464/UTSW-Surgery-Project)

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Funding

Internal research and development funds were employed.

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Correspondence to Eric C. Larson.

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Authors Wang, Dai, Morgan, Elsaied, Garbens, Qu, Steinberg, Gahan, and Larson declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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was obtained from all individual participants included in the study. This article does not contain any studies with animals performed by any of the authors.

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Wang, Y., Dai, J., Morgan, T.N. et al. Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks. J Robotic Surg (2021). https://doi.org/10.1007/s11701-021-01316-2

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  • DOI: https://doi.org/10.1007/s11701-021-01316-2

Keywords

  • Surgical training
  • Robotic-assisted surgery
  • Deep learning
  • Skill evaluation
  • Keypoint detection