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A computer vision technique for automated assessment of surgical performance using surgeons’ console-feed videos

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

Abstract

Purpose

To develop and validate an automated assessment of surgical performance (AASP) system for objective and computerized assessment of pelvic lymph node dissection (PLND) as an integral part of robot-assisted radical cystectomy (RARC) using console-feed videos recorded during live surgery.

Methods

Video recordings of 20 PLNDs were included. The quality of lymph node clearance was assessed based on the features derived from the computer vision process which include: the number and cleared area of the vessels/nerve (N–Vs); image median color map; and mean entropy (measures the level of disorganization) in the video frame. The automated scores were compared to the validated pelvic lymphadenectomy appropriateness and completion evaluation (PLACE) scoring rated by a panel of expert surgeons. Logistic regression analysis was employed to compare automated scores versus PLACE scores.

Results

Fourteen procedures were used to develop the AASP algorithm. A logistic regression model was trained and validated using the aforementioned features with 30% holdout cross-validation. The model was tested on the remaining six procedures, and the accuracy of predicting the expert-based PLACE scores was 83.3%.

Conclusions

To our knowledge, this is the first automated surgical skill assessment tool that provides an objective evaluation of surgical performance with high accuracy compared to expert surgeons’ assessment that can be extended to any endoscopic or robotic video-enabled surgical procedure.

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Acknowledgements

Roswell Park Cancer Institute Alliance Foundation.

Author information

Authors and Affiliations

  1. A.T.L.A.S (Applied Technology Laboratory for Advanced Surgery) Program, Department of Urology, Roswell Park Comprehensive Cancer Center, Elm & Carlton St, Buffalo, NY, 14263, USA

    Amir Baghdadi, Ahmed A. Hussein, Youssef Ahmed & Khurshid A. Guru

  2. Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA

    Amir Baghdadi & Lora A. Cavuoto

Authors
  1. Amir Baghdadi
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  2. Ahmed A. Hussein
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  3. Youssef Ahmed
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  4. Lora A. Cavuoto
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  5. Khurshid A. Guru
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Corresponding author

Correspondence to Khurshid A. Guru.

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The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

The study included the de-identified videos and was considered by the Institutional Review Board at Roswell Park Comprehensive Cancer Center to be a non-human subject research.

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Baghdadi, A., Hussein, A.A., Ahmed, Y. et al. A computer vision technique for automated assessment of surgical performance using surgeons’ console-feed videos. Int J CARS 14, 697–707 (2019). https://doi.org/10.1007/s11548-018-1881-9

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  • DOI: https://doi.org/10.1007/s11548-018-1881-9

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