Evidence map›Paper›PMID 35879572›Full record

ArticleSurgical endoscopy2022

Artificial intelligence for diagnosing gastric lesions under white-light endoscopy.

Xiang-Lei Yuan, Yao Zhou, Wei Liu, Qi Luo, Xian-Hui Zeng, Zhang Yi, Bing Hu

Abstract read
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
3.5field-weighted citation impact, top 6% of its field
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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.

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  10. Artificial intelligence in gastrointestinal surgery: A systematic review.World journal of gastrointestinal surgery · 2025
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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

7 authors at 1 institution in 1 country.

Xiang-Lei Yuan *Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China.
Yao Zhou *Center of Intelligent Medicine, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road Chengdu, Chengdu, 610065, Sichuan, China.
Wei LiuDepartment of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China.
Qi LuoDepartment of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China.
Xian-Hui ZengDepartment of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China.
Zhang YiCenter of Intelligent Medicine, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road Chengdu, Chengdu, 610065, Sichuan, China. zhangyi@scu.edu.cn.
Bing HuDepartment of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China. hubingnj@163.com.ORCID http://orcid.org/0000-0002-9898-8656
Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe ability of endoscopists to identify gastric lesions is uneven. Even experienced endoscopists may miss or misdiagnose lesions due to heavy workload or fatigue or subtle changes in lesions under white-light endoscopy (WLE). This study aimed to develop an artificial intelligence (AI) system that could diagnose six common gastric lesions under WLE and to explore its role in assisting endoscopists in diagnosis.

methodsImages of early gastric cancer, advanced gastric cancer, submucosal tumor, polyp, peptic ulcer, erosion, and lesion-free gastric mucosa were retrospectively collected to train and test the system. The performance of the system was compared with that of 12 endoscopists. The performance of endoscopists with or without referring to the system was also evaluated.

resultsA total of 29,809 images from 8947 patients and 1579 images from 496 patients were used to train and test the system, respectively. For per-lesion analysis, the overall accuracy of the system was 85.7%, which was comparable to that of senior endoscopists (85.1%, P = 0.729) and significantly higher than that of junior endoscopists (78.8%, P < 0.001). With system assistance, the overall accuracies of senior and junior endoscopists increased to 89.3% (4.2%, P < 0.001) and 86.2% (7.4%, P < 0.001), respectively. Senior and junior endoscopists achieved varying degrees of improvement in the diagnostic performance of other types of lesions except for polyp. The diagnostic times of senior (3.8 vs 3.2 s per image, P = 0.500) and junior endoscopists (6.2 vs 4.6 s per image, P = 0.144) assisted by the system were both slightly shortened, despite no significant differences.

conclusionsThe proposed AI system could be applied as an auxiliary tool to reduce the workload of endoscopists and improve the diagnostic accuracy of gastric lesions.

Indexed as

Artificial IntelligenceStomach NeoplasmsEarly Detection of CancerEndoscopyHumansRetrospective StudiesArtificial intelligenceErosionGastric cancerPeptic ulcerPolypSubmucosal tumor

Identifiers

PMID35879572
OpenAlexW4287578560

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.