Evidence map›Paper›PMID 40116897›Full record

ArticleSurgical endoscopy2025

Automated surgical action recognition and competency assessment in laparoscopic cholecystectomy: a proof-of-concept study.

Hung-Hsuan Yen, Yi-Hsiang Hsiao, Meng-Han Yang, Jia-Yuan Huang, Hsu-Ting Lin, Chun-Chieh Huang, Jakey Blue, Ming-Chih Ho

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In one paragraph

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 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Hung-Hsuan YenDepartment of Surgery, National Taiwan University Hospital Hsin-Chu Branch, No. 2, Sec. 1, Shengyi Rd., Zhubei City, Hsinchu County, 302058, Taiwan.
Yi-Hsiang HsiaoDepartment of Surgery, National Taiwan University Hospital Hsin-Chu Branch, No. 2, Sec. 1, Shengyi Rd., Zhubei City, Hsinchu County, 302058, Taiwan.
Meng-Han YangMaster Program in Statistics, National Taiwan University, Taipei, Taiwan.
Jia-Yuan HuangInstitute of Industrial Engineering, National Taiwan University, Taipei, Taiwan.
Hsu-Ting LinACE Biotek Co., Ltd, Hsinchu County, Taiwan.
Chun-Chieh HuangDepartment of Surgery, National Taiwan University Hospital Hsin-Chu Branch, No. 2, Sec. 1, Shengyi Rd., Zhubei City, Hsinchu County, 302058, Taiwan.
Jakey Blue *Institute of Industrial Engineering, National Taiwan University, Taipei, Taiwan.
Ming-Chih Ho *Department of Surgery, National Taiwan University Hospital Hsin-Chu Branch, No. 2, Sec. 1, Shengyi Rd., Zhubei City, Hsinchu County, 302058, Taiwan. mcho1215@ntu.edu.tw.ORCID http://orcid.org/0000-0003-3660-1062

Funding

National Taiwan University Hospital Hsin-Chu Branch HMM36
6 · The paper itself

Abstract

backgroundLaparoscopic cholecystectomy (LC) is a common procedure with standardized steps and validated assessment tools. However, the role of surgical actions in competency assessment remains underexplored, and automated models for surgical action recognition are lacking.

methodsThe Cholec80 dataset of 80 LC videos was analyzed for the Calot's Triangle Dissection (CTD) phase. Strasberg's critical view of safety (CVS) score and second-by-second annotations of surgical actions were evaluated. Videos were categorized into high_simple, low_simple, and high_complex groups based on competency levels and cholecystitis grade. The dataset was randomly divided into training (66 videos) and testing (14 videos) sets based on subgrouping. Surgical metrics were compared between subgroups, and a Random Forest model was constructed to predict competency levels using these metrics. In addition, a Video-Masked Autoencoders (VideoMAE) model was developed for surgical action recognition.

resultsThe high_simple group had significantly shorter CTD duration, fewer action transitions, and lower percentages of suctioning/irrigating, coagulating, and idle actions, but higher CVS scores and dissecting percentages. The Random Forest model achieved 93% accuracy (AUC: 0.96) in competency prediction, with CVS score, CTD duration, and percentages of dissecting, coagulating, and exposing as the top five important features. The VideoMAE model attained 89.11% overall accuracy in recognizing surgical actions, with the highest recall (0.97) for dissecting and the lowest (0.51) for suctioning/irrigating.

conclusionsThis study highlights the importance of surgical actions in competency assessment and presents automated models for evaluation and action recognition. These tools have potential to transform surgical education by providing objective and data-driven feedback for skill improvement.

Indexed as

Cholecystectomy, LaparoscopicClinical CompetenceHumansProof of Concept StudyVideo RecordingArtificial intelligenceCholecystectomyCompetency assessmentMachine learningSurgical action recognition

Identifiers

PMID40116897

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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.