Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery

Background: Our previous work classified a taxonomy of suturing gestures during a vesicourethral anastomosis of robotic radical prostatectomy in association with tissue tears and patient outcomes. Herein, we train deep learning-based computer vision to automate the identification and classification of suturing gestures for needle driving attempts. Methods: Using two independent raters, we manually annotated live suturing video clips to label timepoints and gestures. Identification (2,395 videos) and classification (511 videos) datasets were compiled to train computer vision models to produce 2-and 5-class label predictions, respectively. Networks were trained on inputs of raw red/blue/green pixels as well as optical flow for each frame. Each model was trained on 80/20 train/test splits. Results: In this study, all models were able to reliably predict either the presence of a gesture (identi-fication, area under the curve: 0.88) as well as the type of gesture (classification, area under the curve: 0.87) at significantly above chance levels. For both gesture identification and classification datasets, we observed no effect of recurrent classification model choice (long short-term memory unit versus con-volutional long short-term memory unit) on performance. Conclusion: Our results demonstrate computer vision's ability to recognize features that not only can identify the action of suturing but also distinguish between different classifications of suturing gestures. This demonstrates the potential to utilize deep learning computer vision toward future automation of surgical skill assessment. (c) 2020 Elsevier Inc. All rights reserved.

Automated summary

The study demonstrates the potential of deep learning computer vision to identify and distinguish different classifications of suturing gestures, reliably predicting their types. Different recurrent classification model choices did not affect performance.