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

Search research articles

Related Experiment Video

Updated: Jun 18, 2025

The Role of Indocyanine Green Fluorescence in Complex Laparoscopic Cholecystectomy Navigation

The Role of Indocyanine Green Fluorescence in Complex Laparoscopic Cholecystectomy Navigation

Published on: January 31, 2025

Deep learning-based surgical phase recognition in laparoscopic cholecystectomy.

Hye Yeon Yang¹, Seung Soo Hong², Jihun Yoon³

¹Department of Liver Transplantation and Hepatobiliary and Pancreatic Surgery, Ajou University School of Medicine, Suwon, Korea.

Annals of Hepato-Biliary-Pancreatic Surgery

|July 28, 2024

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same authorSame journal

Bile duct segmental resection versus pancreaticoduodenectomy for middle-third extrahepatic cholangiocarcinoma: A propensity-matched, single-center retrospective analysis.

Annals of hepato-biliary-pancreatic surgery·2026

Same author

Robotic single-port plus one-port radical cholecystectomy: description of surgical techniques with video.

Journal of minimally invasive surgery·2026

Same author

Utility of the Iwate Difficulty Scoring System for the Stratification of Laparoscopic Right Hepatectomies: An International Multicenter Study.

Journal of hepato-biliary-pancreatic sciences·2026

Same author

Development of an artificial intelligence-based prediction platform for early recurrence of resectable pancreatic cancer after curative surgery-toward future use as an indication for neoadjuvant treatment: a retrospective multicenter cohort study.

Annals of surgical treatment and research·2026

Same author

Comparison of Long-Term Oncologic and Perioperative Outcomes of Minimally Invasive and Open Pancreatoduodenectomy for Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma: Exploring Type 0 Resection as a Potential Indication for MIPD.

Journal of hepato-biliary-pancreatic sciences·2026

Same author

Prospective Evaluation of Irreversible Electroporation With Clustered Electrodes as a Novel Palliative Approach for Locally Advanced Pancreatic Cancer.

Korean journal of radiology·2026

This summary is machine-generated.

A new artificial intelligence (AI) model accurately identifies surgical phases in laparoscopic cholecystectomy videos. This AI tool enhances surgical workflow analysis and performance evaluation in minimally invasive surgery.

Area of Science:

Medical technology
Artificial intelligence in surgery
Surgical workflow analysis

Background:

Artificial intelligence (AI) is increasingly used for surgical assessment and performance evaluation.
Automating surgical workflow analysis from video is crucial for effective surgical evaluation.
Minimally invasive surgery relies on objective performance metrics.

Purpose of the Study:

To design a deep learning model for automatic surgical phase identification in laparoscopic cholecystectomy.
To evaluate the accuracy of the AI model in recognizing different surgical phases.
To assess AI's potential in objective surgical performance evaluation.

Main Methods:

Utilized a combined dataset of 160 laparoscopic cholecystectomy videos (Cholec80 and institutional data).

Keywords:

Artificial intelligence Computer terminals Laparoscopic cholecystectomy Pattern recognition, automated Surgical procedures, operative

More Related Videos

Author Spotlight: Advancing Hepatobiliary and Pancreatic Tumor Treatment with Minimally Invasive Surgical Techniques

Author Spotlight: Advancing Hepatobiliary and Pancreatic Tumor Treatment with Minimally Invasive Surgical Techniques

Published on: September 27, 2024

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach

Published on: May 23, 2025

Related Experiment Videos

Last Updated: Jun 18, 2025

The Role of Indocyanine Green Fluorescence in Complex Laparoscopic Cholecystectomy Navigation

The Role of Indocyanine Green Fluorescence in Complex Laparoscopic Cholecystectomy Navigation

Published on: January 31, 2025

Author Spotlight: Advancing Hepatobiliary and Pancreatic Tumor Treatment with Minimally Invasive Surgical Techniques

Author Spotlight: Advancing Hepatobiliary and Pancreatic Tumor Treatment with Minimally Invasive Surgical Techniques

Published on: September 27, 2024

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach

Published on: May 23, 2025

Split data into training and testing sets at a 2:1 ratio for AI model development.

Evaluated model performance without pre- or post-processing to isolate AI's impact.

Main Results:

The AI model achieved an overall accuracy of 91.2% on 98,234 test frames.
Highest accuracy was observed in Calot's triangle dissection (F1 score: 0.9421).
Clipping and cutting phase recognition was the least accurate (F1 score: 0.7761).

Conclusions:

The developed AI model demonstrates high accuracy in identifying surgical phases during laparoscopic cholecystectomy.
This technology holds promise for objective and automated surgical performance assessment.
Further refinement could improve accuracy in specific surgical sub-tasks.