Evidence map›Paper›PMID 41038910›Full record

ArticleScientific reports2025

Interpretable deep learning model diagnoses gastrointestinal stromal tumors and lesion characteristics with microprobe endoscopic ultrasonography.

Jiao Li, Xiaojuan Jing, Qin Zhang, Xiaoxiang Wang, Li Wang, Jing Shan, Zhengkui Zhou, Dandan Jiang, Yongfeng Yan, Liu Liu and 5 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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.

Jiao Li *Department of Gastroenterology, The Third People's Hospital of Chengdu, Clinical College of Southwest Jiao Tong University, The Affiliated Hospital of Southwest Jiaotong University, Qinglong street 82#, Chengdu, Sichuan, China.
Xiaojuan Jing *Department of Gastroenterology, The Suining Central Hospital, Suining, China.
Qin Zhang *Department of Gastroenterology, The First People's Hospital of Liangshan Yi Autonomous Prefecture, Xichang, China.
Xiaoxiang Wang *Department of Gastroenterology, The First People's Hospital of Chengdu, Chengdu, China.
Li Wang *Department of Gastroenterology, Sichuan Mianyang 404 Hospital, Mianyang, China.
Jing ShanDepartment of Gastroenterology, The Third People's Hospital of Chengdu, Clinical College of Southwest Jiao Tong University, The Affiliated Hospital of Southwest Jiaotong University, Qinglong street 82#, Chengdu, Sichuan, China.
Zhengkui ZhouDepartment of Gastroenterology, The Third People's Hospital of Chengdu, Clinical College of Southwest Jiao Tong University, The Affiliated Hospital of Southwest Jiaotong University, Qinglong street 82#, Chengdu, Sichuan, China.
Dandan JiangDepartment of Gastroenterology, The Suining Central Hospital, Suining, China.
Yongfeng YanDepartment of Gastroenterology, The First People's Hospital of Liangshan Yi Autonomous Prefecture, Xichang, China.
Liu LiuDepartment of Gastroenterology, The Third People's Hospital of Chengdu, Clinical College of Southwest Jiao Tong University, The Affiliated Hospital of Southwest Jiaotong University, Qinglong street 82#, Chengdu, Sichuan, China.
Ming ZhaoDepartment of Gastroenterology, The Third People's Hospital of Chengdu, Clinical College of Southwest Jiao Tong University, The Affiliated Hospital of Southwest Jiaotong University, Qinglong street 82#, Chengdu, Sichuan, China.
Lin FanSchool of Computing and Artificial Intelligence, Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu, China.
Cenyang ZhengSchool of Computing and Artificial Intelligence, Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu, China.
Xun GongSchool of Computing and Artificial Intelligence, Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu, China. xgong@swjtu.edu.cn.
Xiaobin SunDepartment of Gastroenterology, The Third People's Hospital of Chengdu, Clinical College of Southwest Jiao Tong University, The Affiliated Hospital of Southwest Jiaotong University, Qinglong street 82#, Chengdu, Sichuan, China. xbsun1197@163.com.

Funding

he Health Family Planning Commission of Chengdu 2024077the National Institute of Hospital Administration YLXX24AIA011the National Natural Science Foundation of China 62376231
6 · The paper itself

Abstract

Enhancing diagnostic capability of microprobe endoscopic ultrasonography (MEUS) for GISTs is clinical significance. Despite the promise of artificial intelligence (AI) in aiding diagnosis, challenges remain in model interpretability, generalization and diagnostic specificity. To address this, lesion characteristics were integrated into MEUS images to align AI inference with clinical diagnostic workflow, leading to the development of seven deep learning models. The top model, named ECMAI-ME, was assessed in two external validation sets for generalizability and compared with endoscopists in multicenter test for effectiveness. Using 9,229 MEUS images from 873 SELs across five Chinese hospitals, training involved 522 SELs, with 95 and 93 SELs from different sources for external validation, and 163 SELs for a multicenter test. ECMAI-ME achieved an AUC of 0.972 for case classification internally. In two external validation sets, ECMAI-ME maintained consistency diagnostic performance. In multicenter test, ECMAI-ME significantly outperformed endoscopists in accuracy (85.28% vs. 56.44-77.91%, p<0.05) and specificity (89.19% vs. 52.25-72.92%, p<0.001) with comparable sensitivity, and demonstrating high accuracy in distinguishing lesion echogenicity (96.93%), originating layer (88.34%), and echo heterogeneity (77.30%). This interpretable model offers high specificity, adaptability across hospitals and equipment, and strong potential for integration into clinical workflows.

Indexed as

Deep LearningEndosonographyGastrointestinal NeoplasmsGastrointestinal Stromal TumorsAgedFemaleHumansMaleMiddle AgedSensitivity and SpecificityArtificial intelligenceEndoscopic ultrasonographyGastrointestinal stromal tumorsSubepithelial lesions

Identifiers

PMID41038910
PMCPMC12491597

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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.