Video-based robotic surgical action recognition and skills assessment on porcine models using deep learning.

ABSTRACT:

Objectives

This study aimed to develop an automated skills assessment tool for surgical trainees using deep learning.

Background

Optimal surgical performance in robot-assisted surgery (RAS) is essential for ensuring good surgical outcomes. This requires effective training of new surgeons, which currently relies on supervision and skill assessment by experienced surgeons. Artificial Intelligence (AI) presents an opportunity to augment existing human-based assessments.

Methods

We used a network architecture consisting of a convolutional neural network combined with a long short-term memory (LSTM) layer to create two networks for the extraction and analysis of spatial and temporal features from video recordings of surgical procedures, facilitating action recognition and

SUBMITTER: Hashemi N 

PROVIDER: S-EPMC11870904 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature