Evidence map›Paper›PMID 41480324›Full record

ArticleWorld journal of gastroenterology2025

Artificial intelligence-assisted diagnosis of rectal neuroendocrine tumors during white-light endoscopy.

Ke Liu, Zhen-Yu Wang, Li-Zhi Yi, Feng Li, Shun-Hui He, Xi-Gang Zhang, Chun-Xiao Lai, Zhi-Jian Li, Lin Qiu, Rui-Ya Zhang and 13 more

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

Article in World journal of gastroenterology, 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. 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

23 authors.

Ke LiuDepartment of Gastroenterology, The People's Hospital of Leshan, Southwest Medical University, Leshan 614000, Sichuan Province, China.
Zhen-Yu WangDepartment of Digestive Endoscope, The First Affiliated Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China.
Li-Zhi YiDepartment of Gastroenterology, The People's Hospital of Leshan, Southwest Medical University, Leshan 614000, Sichuan Province, China.
Feng LiDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
Shun-Hui HeDepartment of Gastroenterology, The Eighth Affiliated Hospital of Southern Medical University, Guangzhou 510515, Guangdong Province, China.
Xi-Gang ZhangDepartment of Gastroenterology, Shenzhen Second People's Hospital, Shenzhen 518020, Guangdong Province, China.
Chun-Xiao LaiGastrointestinal Cancer Center, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
Zhi-Jian LiDepartment of Gastroenterology, The Eighth Affiliated Hospital of Southern Medical University, Guangzhou 510515, Guangdong Province, China.
Lin QiuDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
Rui-Ya ZhangDepartment of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China.
Wen WuDepartment of Gastroenterology, Shanxi Academy of Traditional Chinese Medicine, Taiyuan 030024, Shanxi Province, China.
Yu LinDepartment of Gastroenterology, Southern Medical University Hospital of Integrated Traditional Chinese and Western Medicine, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
Hui YangDepartment of Spleen and Stomach, Rizhao Hospital of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Rizhao 276800, Shandong Province, China.
Gui-Ming LiuDepartment of Gastroenterology, Shayang Hospital of Traditional Chinese Medicine, Jingmen 448200, Hubei Province, China.
Quan-Sheng GuanDepartment of Gastroenterology, Shayang Hospital of Traditional Chinese Medicine, Jingmen 448200, Hubei Province, China.
Zhi-Fang ZhaoDepartment of Gastroenterology, National Institution of Drug Clinical Trial, Guizhou Provincial People's Hospital, Medical College of Guizhou University, Guiyang 550000, Guizhou Province, China.
Li-Ming ChengDepartment of Internal Medicine, Taihe People's Hospital Baiyun District, Guangzhou 510515, Guangdong Province, China.
Jie DaiSuzhou Wellomen Information Technology Co. Ltd., Suzhou 215000, Jiangsu Province, China.
Yang BaiDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
Fang XieDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
Meng-Nan ZhangDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
Su-Zuan ChenDepartment of Digestive Endoscope, The First Affiliated Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China.
Xian-Fei ZhongDepartment of Gastroenterology, The People's Hospital of Leshan, Southwest Medical University, Leshan 614000, Sichuan Province, China. 13981302161@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDue to their significantly lower incidence than colorectal polyps and macroscopic features resembling those of hyperplastic polyps, rectal neuroendocrine tumors (rNETs) are frequently misdiagnosed and resected as polyps. To date, no reports have been written on the application of artificial intelligence for assisting in the white-light endoscopy of rNETs.

aimTo establish a neuroendocrine tumor lesion detection algorithm based on the YOLOv7 model and evaluate the performance of the algorithm in identifying neuroendocrine tumors.

methodsIn total, 137748 white-light endoscopic images were collected in this study, including 2232 images of rNET, 4429 images of submucosal lesions other than rNET, 42563 images of polyps, and 88593 images of normal mucosa. All the images were randomly divided into a training set, a validation set, and a test set. To evaluate the ability of the algorithm to diagnose rNETs, we selected 1578 images to form the test set. The performance of the algorithm was compared with that of endoscopists at different levels.

resultsThe accuracy of the algorithm in identifying rNET from all the images was 97.8%, the sensitivity was 72.6%, the specificity was 99.7%, the positive predictive value was 93.9%, and the negative predictive value was 98.1%.

conclusionOur model, which was based on YOLOv7, could effectively detect rNET lesions, which was better than that of most endoscopists.

Indexed as

Artificial IntelligenceNeuroendocrine TumorsRectal NeoplasmsAlgorithmsFemaleHumansMaleMiddle AgedPredictive Value of TestsRectumSensitivity and SpecificityArtificial intelligenceEndoscopyPolypsRectal neuroendocrine tumorsWhite light

Identifiers

PMID41480324
PMCPMC12754245

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