  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 (CV) to automate the identification and classification of
suturing gestures for needle driving attempts. Using two independent raters, we
manually annotated live suturing video clips to label timepoints and gestures.
Identification (2395 videos) and classification (511 videos) datasets were
compiled to train CV models to produce two- and five-class label predictions,
respectively. Networks were trained on inputs of raw RGB pixels as well as
optical flow for each frame. Each model was trained on 80/20 train/test splits.
In this study, all models were able to reliably predict either the presence of
a gesture (identification, AUC: 0.88) as well as the type of gesture
(classification, AUC: 0.87) at significantly above chance levels. For both
gesture identification and classification datasets, we observed no effect of
recurrent classification model choice (LSTM vs. convLSTM) on performance. Our
results demonstrate CV'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 CV towards future automation of surgical skill
assessment.
