Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Building Capacity for Surgical Artificial Intelligence Research.

Journal of the American College of Surgeons·2026

Same author

Regulating digital surgery to balance safety and innovation: a SAGES white paper.

Surgical endoscopy·2026

Same author

Risk and liability in the deployment of AI systems for surgery: a SAGES white paper.

Surgical endoscopy·2026

Same author

Surgeon Perceptions of Failure to Rescue After Surgery.

JAMA network open·2026

Same author

Live surgery in body donors with interactive digital technologies for innovative and interdisciplinary teaching of surgical anatomy.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy·2026

Same author

Standardization of surgical gesture taxonomy: a SAGES Delphi consensus study.

Surgical endoscopy·2026

Same journal

The International Medical Graduate Paradox.

Annals of surgery·2026

Same journal

Defining the Incremental Value of Endoscopic Ultrasound in Assessing Pancreatic Cystic Neoplasms.

Annals of surgery·2026

Same journal

Trends in Metabolic and Bariatric Surgery and GLP-1 Receptor Agonist Use Among Adolescents with Severe Obesity.

Annals of surgery·2026

Same journal

The Ambulatory Surgery Center Paradox: Why 60% of Surgeries Occur Where 2% of AI Research Happens.

Annals of surgery·2026

Same journal

Medical Student First Authorship in High-Impact Surgical Journals: Longitudinal Trends and Institutional Concentration, 2000-2025.

Annals of surgery·2026

Same journal

Radial Margin Distance in Perihilar Cholangiocarcinoma: Defining Dual Cutoff Values of 0 and 1 mm.

Annals of surgery·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 30, 2025

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Published on: September 2, 2025

Machine Learning for Surgical Phase Recognition: A Systematic Review.

Carly R Garrow¹, Karl-Friedrich Kowalewski^1,2, Linhong Li¹

¹Department of General, Visceral, and Transplantation Surgery, University Hospital of Heidelberg, Heidelberg, Germany.

Annals of Surgery

|November 17, 2020

Summary

This summary is machine-generated.

Machine learning models can accurately recognize surgical phases using various data streams. This automation aids surgical training, workflow optimization, and patient safety, though manual annotation is often required.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Mixed Reality Assisted Radical Endoscopic Thyroidectomy

Mixed Reality Assisted Radical Endoscopic Thyroidectomy

Published on: January 31, 2025

Related Experiment Videos

Last Updated: Nov 30, 2025

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Published on: September 2, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Mixed Reality Assisted Radical Endoscopic Thyroidectomy

Mixed Reality Assisted Radical Endoscopic Thyroidectomy

Published on: January 31, 2025

Area of Science:

Surgical Innovation
Artificial Intelligence in Medicine
Data Science in Healthcare

Background:

Surgical phase recognition is crucial for understanding operative steps.
Machine learning (ML) offers advanced data analysis for surgical insights.
Automated phase recognition enhances surgical training, workflow, and patient safety.

Purpose of the Study:

To review ML models used for automated surgical phase recognition.
To identify data streams employed in surgical phase recognition.
To provide an overview of current advancements in the field.

Main Methods:

Systematic review following Cochrane and PRISMA guidelines.
Searched PubMed, Web of Science, IEEExplore, Google Scholar, and Cite SeerX.
Included literature on ML models and intraoperative signals for general surgery phase recognition.

Main Results:

35 full-text articles were included from 2254 titles/abstracts.
Hidden Markov Models and Artificial Neural Networks were common ML models.
Surgical videos and instrument usage data were frequently used, achieving >90% accuracy in some cases, notably in laparoscopic cholecystectomy.

Conclusions:

ML models demonstrate high accuracy for surgical phase recognition.
Intraoperative data like video and instrument type are effective inputs.
Future ML applications promise standardized, efficient, and objective surgical workflows for improved patient outcomes.