Evidence map›Paper›PMID 42528629›Full record

ArticleFrontiers in gastroenterology (Lausanne, Switzerland)2026

Limited discriminative performance of endoscopic deep learning for

Wenyi Zhou, Yixing Wang, Yuhong Wang, Yongluo Jiang, Wencan He, Binbin Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in gastroenterology (Lausanne, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Wenyi Zhou *Radiology Department, Changsha Stomatological Hospital, Changsha, China.
Yixing Wang *Department of Medical Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China.
Yuhong WangDepartment of Endoscopy, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China.
Yongluo JiangDepartment of Nuclear Medicine, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China.
Wencan HePrecision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Binbin XuEuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Methods: We retrospectively collected 602 endoscopic images from 337 patients with gastric cancer treated at a tertiary cancer center. After preprocessing and quality control, 576 images from 329 patients were retained for model development and evaluation. Results: Model discrimination was limited. The best-performing approach achieved a median test AUC of 0.6255, with sensitivity of 0.7308, specificity of 0.3714, accuracy of 0.6092, and F1 score of 0.6800. Fine-tuning showed a higher median AUC than retraining, but discrimination remained limited in both strategies. Conclusions: In this retrospective gastric cancer cohort, endoscopic image-based classification showed limited ability to discriminate

Indexed as

endoscopic imaginggastric cancerHelicobacter pyloriimage classificationneural networks

Identifiers

PMID42528629
PMCPMC13414106

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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.