Journal article
Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks
Journal of robotic surgery, Vol.16(4), pp.917-925
08/01/2022
DOI: 10.1007/s11701-021-01316-2
PMID: 34709538
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.
Details
- Title: Subtitle
- Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks
- Creators
- Yihao Wang - Southern Methodist UniversityJessica Dai - The University of Texas Southwestern Medical CenterTara N. Morgan - The University of Texas Southwestern Medical CenterMohamed Elsaied - Southern Methodist UniversityAlaina Garbens - The University of Texas Southwestern Medical CenterXingming Qu - Southern Methodist UniversityRyan Steinberg - The University of Texas Southwestern Medical CenterJeffrey Gahan - The University of Texas Southwestern Medical CenterEric C. Larson - Southern Methodist University
- Resource Type
- Journal article
- Publication Details
- Journal of robotic surgery, Vol.16(4), pp.917-925
- Publisher
- Springer Nature
- DOI
- 10.1007/s11701-021-01316-2
- PMID
- 34709538
- ISSN
- 1863-2483
- eISSN
- 1863-2491
- Number of pages
- 9
- Language
- English
- Date published
- 08/01/2022
- Academic Unit
- Urology
- Record Identifier
- 9984320077602771
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