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

arXiv: https://arxiv.org/abs/2008.11833 · HTML: https://mlsanity.com/paper/2008.11833/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Towards Unified Surgical Skill Assessment](https://mlsanity.com/paper/2106.01035/) (2021)
- [Gesture Recognition in Robotic Surgery: a Review](https://mlsanity.com/paper/2102.00027/) (2021)
- [Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding](https://mlsanity.com/paper/2303.18019/) (2023)
- [Quantification of Robotic Surgeries with Vision-Based Deep Learning](https://mlsanity.com/paper/2205.03028/) (2022)
- [Computer Vision for Increased Operative Efficiency via Identification of Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study](https://mlsanity.com/paper/2312.03001/) (2023)

## Abstract (arXiv)

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.