Evidence map›Paper›PMID 40542142›Full record

ArticleSurgical endoscopy2025

Artificial intelligence-based marks detection and incision guide line prediction model in esophageal endoscopic submucosal dissection: a multicenter study (with video).

Ruide Liu, Xianglei Yuan, Kaide Huang, Qi Luo, Nuoya Zhou, Chuncheng Wu, Tingfa Peng, Wanhong Zhang, Xiaogang Bi, Xin Chen and 5 more

Abstract readEvaluation StudyMulticenter StudyVideo-Audio Media
PubMed Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

15 authors.

Ruide Liu *Department of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China.
Xianglei Yuan *Department of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China.
Kaide Huang *Center of Intelligent Medicine, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, Sichuan, China.
Qi LuoDepartment of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China.
Nuoya ZhouDepartment of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China.
Chuncheng WuDepartment of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China.
Tingfa PengDepartment of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China.
Wanhong ZhangDepartment of Gastroenterology, Cangxi People's Hospital, Guangyuan, China.
Xiaogang BiDepartment of Gastroenterology, Zigong Fourth People's Hospital, Zigong, China.
Xin ChenDepartment of Gastroenterology, Pangang Group General Hospital, Panzhihua, China.
Wei WeiDepartment of Gastroenterology, The First People's Hospital of Zigong, Zigong, China.
Yinong ZhuDepartment of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China.
Lifan ZhangDepartment of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China.
Zhang Yi *Center of Intelligent Medicine, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, Sichuan, China. zhangyi@scu.edu.cn.
Bing Hu *Department of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China. hubing@wchscu.edu.cn.ORCID http://orcid.org/0009-0007-2272-747X

Funding

National Natural Science Foundation of China 62406205National Natural Science Foundation of China 82170675National Natural Science Foundation of China U24A20341Postdoctor Research Fund of West China Hospital, Sichuan University 2024HXBH164Sichuan Province cadres health research ZH2024-102Sichuan Science and Technology Program 2025ZNSFSC1687
6 · The paper itself

Abstract

backgroundEndoscopic submucosal dissection (ESD) was an important minimally invasive procedure for treating early esophageal cancer, where the surrounding mucosal incision (SMI) was a crucial yet challenging phase. This study aimed to develop an artificial intelligence (AI) model for marks detection and incision guide line prediction during SMI, and to evaluate its performance compared to multicenter endoscopists.

methodsImages were extracted at one frame per second from video clips and divided into 3 datasets: training dataset, internal and external test dataset. Twenty-two endoscopists participated in the comparison with the model, including eight senior and fourteen junior endoscopists. Multiple objective indicators and a marks-guide line subjective score (M-GSS) were used for outcomes.

resultsA total of 46,280 SMI images were extracted from 166 esophageal ESD videos in 4 hospitals between 2016 and 2024. For marks detection, the precision of the AI model in the training dataset, internal, and external test datasets were 78.97%, 85.76%, and 79.46%. For the incision guide line, the average distance error (ADE) was 0.096, 0.097, and 0.159, respectively. The AI model achieved an accuracy (63.21% vs 54.57%, P < 0.001), ADE (0.081 vs 0.241, P = 0.002), and M-GSS (1.88 vs 1.75, P < 0.001) significantly better than that of junior endoscopists, and were comparable with those of senior endoscopists.

conclusionsThe AI model achieved promising results on large SMI image datasets, showing comparable effectiveness with senior endoscopists for marks detection and incision guide line, which had the potential to navigate SMI safely and properly.

Indexed as

Artificial IntelligenceEndoscopic Mucosal ResectionEsophageal NeoplasmsEsophagoscopyClinical CompetenceFemaleHumansMaleVideo RecordingArtificial intelligenceEndoscopic submucosal dissectionIncision guide lineMarksSurrounding mucosal incision

Identifiers

PMID40542142

What OpenQuestion holds

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Registered trials

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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.