Evidence map›Paper›PMID 40335821›Full record

ArticleSurgical endoscopy2025

Artificial intelligence-assisted endoscopic ultrasound diagnosis of esophageal subepithelial lesions.

Ai-Meng Zhang, Dai-Min Jiang, Shu-Peng Wang, Wen Liu, Bei-Bei Sun, Zhe Wang, Guo-Yi Zhou, Yao-Fu Wu, Qing-Yun Cai, Jin-Tao Guo and 1 more

Abstract read
PubMed Full text at PMC 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

11 authors.

Ai-Meng ZhangDepartment of Gastroenterology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Shenyang, 110004, Liaoning Province, China.
Dai-Min JiangResearch Center for Innovation, SonoScape Medical Corporation, Shenzhen, 518107, Guangdong Province, China.
Shu-Peng WangDepartment of Gastroenterology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Shenyang, 110004, Liaoning Province, China.
Wen LiuDepartment of Gastroenterology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Shenyang, 110004, Liaoning Province, China.
Bei-Bei SunDepartment of Gastroenterology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Shenyang, 110004, Liaoning Province, China.
Zhe WangDepartment of Pathology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning Province, China.
Guo-Yi ZhouResearch Center for Innovation, SonoScape Medical Corporation, Shenzhen, 518107, Guangdong Province, China.
Yao-Fu WuDigital Information Development Department, SonoScape Medical Corporation, Shenzhen, 518107, Guangdong Province, China.
Qing-Yun CaiProduct Management Department, SonoScape Medical Corporation, Shenzhen, 518107, Guangdong Province, China.
Jin-Tao GuoDepartment of Gastroenterology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Shenyang, 110004, Liaoning Province, China. guojt@sj-hospital.org.ORCID http://orcid.org/0000-0001-5722-6359
Si-Yu SunDepartment of Gastroenterology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Shenyang, 110004, Liaoning Province, China.

Funding

Liaoning Province Applied Basic Research Program Joint Program Project 2022JH2/101500076Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program Grant No. RC200438Tree planting program of Shengjing Hospital M1595
6 · The paper itself

Abstract

backgroundEndoscopic ultrasound (EUS) is one of the most accurate methods for determining the originating layer of subepithelial lesions (SELs). However, the accuracy is greatly influenced by the expertise and proficiency of the endoscopist. In this study, we aimed to develop an artificial intelligence (AI) model to identify the originating layer of SELs in the esophagus and evaluate its efficacy.

methodsA total of 1445 cases of esophageal SELs were used to develop the model. An AI model stemming from YOLOv8s-seg and MobileNetv2 was developed to detect esophageal lesions and identify the originating layer. Two seniors and two junior endoscopists independently diagnosed the same test set.

resultsThe precision, recall, mean average precision @ 0.5, and F1-score of the AI model were 92.2%, 73.6%, 0.832, and 81.9%, respectively. The overall accuracy of the originating layer recognition model was 55.2%. The F1-scores of the second, third, and fourth layers were 47.1%, 51.7%, and 66.1%, respectively. The accuracy of the AI system in differentiating layers 2 and 3 from four was 76.5% and was similar to that of senior endoscopists (74.9-79.8%, P = 0.585) but higher than that of junior endoscopists (65.6-66.7%, P = 0.045).

conclusionsThe EUS-AI model has shown high diagnostic potential for detecting esophageal SELs and identifying their originating layers. EUS-AI has the potential to enhance the diagnostic ability of junior endoscopists in clinical practice.

Indexed as

Artificial IntelligenceEndosonographyEsophageal DiseasesEsophageal NeoplasmsAdultAgedEsophagusFemaleHumansMaleMiddle AgedArtificial intelligenceEndoscopic ultrasoundEsophagusSubepithelial lesion

Identifiers

PMID40335821
PMCPMC12116721

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LicenceCC BY
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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.