Development of an artificial intelligence algorithm for automated surgical gestures annotation.

ABSTRACT: Surgical gestures analysis is a promising method to assess surgical procedure quality, but manual annotation is time-consuming. We aimed to develop a recurrent neural network for automated surgical gesture annotation using simulated robot-assisted radical prostatectomies. We have previously manually annotated 161 videos with five different surgical gestures (Regular dissection, Hemostatic control, Clip application, Needle handling, and Suturing). We created a model consisting of two neural networks: a pre-trained feature extractor (VisionTransformer using Imagenet) and a classification head (recurrent neural network with a Long Short-Term Memory (LSTM(128) and fully connected layer)). The data set was split into a training + validation set and a test set. The trained model labeled input se

SUBMITTER: Olsen RG 

PROVIDER: S-EPMC12274238 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature