ArticleSurgical endoscopy2025
Video-based robotic surgical action recognition and skills assessment on porcine models using deep learning.
Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Artificial intelligence-based action recognition and skill assessment in robotic cardiac surgery simulation: a feasibility study.Journal of robotic surgery · 2025Trial
- The role of artificial intelligence in advancing urologic care: From diagnostics to therapeutics.Surgery in practice and science · 2026Review
- Egocentric video analysis for automated assessment of open surgical skills via deep learning.International journal of computer assisted radiology and surgery · 2026Article
- Robotic hepatopancreatobiliary surgery using the Toumai® system: initial experience and technical considerations from a single center.Updates in surgery · 2025Article
- Closing the data gap: leveraging pretrained neural networks for robotic surgical assessment on limited clinical data.Journal of robotic surgery · 2025Article
- Artificial Intelligence and Digital Tools Across the Hepato-Pancreato-Biliary Surgical Pathway: A Systematic Review.Journal of clinical medicine · 2025Review
- Video-based robotic surgical action recognition and skills assessment on porcine models using deep learning.Surgical endoscopy · 2025Article
- Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.American journal of clinical and experimental urology · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectivesThis study aimed to develop an automated skills assessment tool for surgical trainees using deep learning.
backgroundOptimal 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.
methodsWe 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 skill assessment.
results21 participants (16 novices and 5 experienced) performed 16 different intra-abdominal robot-assisted surgical procedures on porcine models. The action recognition network achieved an accuracy of 96.0% in identifying surgical actions. A GradCAM filter was used to enhance the model interpretability. The skill assessment network had an accuracy of 81.3% in classifying novices and experiences. Procedure plots were created to visualize the skill assessment.
conclusionOur study demonstrated that AI can be used to automate surgical action recognition and skill assessment. The use of a porcine model enables effective data collection at different levels of surgical performance, which is normally not available in the clinical setting. Future studies need to test how well AI developed within a porcine setting can be used to detect errors and provide feedback and actionable skills assessment in the clinical setting.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.