Evidence map›Paper›PMID 40610638›Full record

ArticleSurgical endoscopy2025

Surgical skill assessment using an AI-based surgical phase recognition model for laparoscopic cholecystectomy.

Yoshitsugu Yanagida, Shin Takenaka, Daichi Kitaguchi, Shogo Hamano, Atsuki Tanaka, Haruki Mitarai, Raito Suzuki, Kimimasa Sasaki, Nobuyoshi Takeshita, Tetsuya Ishimaru and 2 more

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

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
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

12 authors.

Yoshitsugu YanagidaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Shin TakenakaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Daichi KitaguchiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Shogo HamanoDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Atsuki TanakaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Haruki MitaraiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Raito SuzukiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Kimimasa SasakiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Nobuyoshi TakeshitaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Tetsuya IshimaruDepartment of Pediatric Surgery, National Center for Child Health and Development, 2-10-1 Ookura, Setagaya-ku, Tokyo, 157-8535, Japan.
Jun FujishiroDepartment of Pediatric Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Masaaki ItoDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. maito@east.ncc.go.jp.ORCID http://orcid.org/0000-0002-1101-2707

Funding

Japan Agency for Medical Research and Development JP21vk0124001
6 · The paper itself

Abstract

backgroundAutomated surgical skill assessment using artificial intelligence (AI) in laparoscopic cholecystectomy (Lap-C) can be a valuable method for improving the effectiveness of surgical education and enhancing the surgical outcomes of Lap-C. This study aimed to assess the applicability of automated surgical skill assessment using a developed AI-based surgical phase recognition model in Lap-C.

methodsWe collected Lap-C videos and categorized them into training and test datasets. The training dataset was used to develop a surgical phase recognition model that classified the Lap-C procedure into 10 phases. The test dataset was categorized into three surgical skill levels (expert, intermediate, and novice groups) based on predefined criteria. We evaluated the applicability of our model to automatically categorize the three surgical skill levels using parameters derived from the model, including time spent in each phase and the confidence level for phase recognition (AI confidence score [AICS]).

resultsThe overall accuracy of the surgical phase recognition model was 82.3%. Manual analysis showed that the time for dissection in the gallbladder neck in the novice group was significantly longer than in the expert group (P < 0.01), and a similar trend was observed in the model-based analysis. The time for clipping and cutting the cystic duct and artery in the novice group was significantly longer than in the expert group in both manual and model-based analyses. AICS was higher in the expert group than in the intermediate group (P = 0.02).

conclusionWe developed an automated surgical phase recognition model in Lap-C with AI, which was applicable for surgical skill assessment by measuring the time required for dissection of the gallbladder neck, clipping and cutting the cystic duct and artery, and calculating the AICS. Our model is expected to contribute to the efficiency of surgical education.

Indexed as

Artificial IntelligenceCholecystectomy, LaparoscopicClinical CompetenceFemaleHumansMaleVideo RecordingAI confidence scoreArtificial intelligenceLaparoscopic cholecystectomySurgical phase recognitionSurgical skill assessment

Identifiers

PMID40610638

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.