Evidence map›Paper›PMID 39843600›Full record

ArticleSurgical endoscopy2025

A prospective and comparative study on improving the diagnostic accuracy of early gastric cancer based on deep convolutional neural network real-time diagnosis system (with video).

Jie Feng, Yaoping Zhang, Zhijun Feng, Huiming Ma, Yani Gou, Pengfei Wang, Yanhu Feng, Xiang Wang, Xiaojun Huang

Abstract readComparative Study
PubMed 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 6 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 3 pooled it
–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

6 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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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

9 authors.

Jie FengDepartment of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China. fengj05@163.com.
Yaoping ZhangDepartment of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China.
Zhijun FengSouthern Medical University, Guangzhou, Guangdong Province, China.
Huiming MaDepartment of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China.
Yani GouDepartment of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China.
Pengfei WangDepartment of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China.
Yanhu FengDepartment of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China.
Xiang WangDepartment of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China.
Xiaojun HuangDepartment of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China.

Funding

Gansu Education Department 2021B-034
6 · The paper itself

Abstract

backgroundEndoscopic diagnosis of early gastric cancer (EGC) is a challenge. It is not clear whether deep convolutional neural network (DCNN) model could improve the endoscopists' diagnostic performance.

methodsWe established a DCNN-assisted system and found that accuracy of diagnosis is higher than endoscopists. We prospectively collected an independent image test set of 1289 images and a video test set of 130 patients from three endoscopic centers to compare the diagnostic efficacy of 12 endoscopists before and after DCNN model assistance. Accuracy, sensitivity, specificity, time, and AUC were the main indicators for comparison.

resultsThe DCNN model discriminated EGC from the control group (including ulcers and chronic gastritis) with an AUC of 0.917, a sensitivity of 93.38% (95% CI 91.09-95.12%), and a specificity of 90.07% (95% CI 87.59-92.10%) in the image dataset. The video test dataset have an AUC of 0.930, a sensitivity of 96.92% (95% CI 88.83-99.78%), and a specificity of 89.23% (95% CI 79.11-94.98%). The diagnostic performance of novice endoscopists was comparable to those of expert endoscopists with the DCNN model's assistance (accuracy: 95.22 vs. 96.16%) in image test dataset. In the video test, the novice endoscopists, accuracy after DCNN assistance was also improved from 79.36 to 86.41%, and from 86.28 to 91.03% for expert endoscopists. The mean pairwise kappa value of endoscopists was increased significantly with the DCNN model's assistance (0.705-0.753 vs.0.767-0.890) in image testing, and (0.657-0.793 vs. 0.738-0.905) in video testing. The diagnostic duration reduced considerably with the assistance of the DCNN model from 7.09 ± 0.6 s to 5.05 ± 0.55 s in image test, and from 2392.17 ± 7.77 s to2378.34 ± 23.51 s in video test.

conclusionWe developed a DCNN-assisted diagnostic system. And the system can improve the diagnostic performance of endoscopists and help novice endoscopists achieve diagnostic accuracy comparable to that of expert endoscopists.

Indexed as

Deep LearningEarly Detection of CancerGastroscopyNeural Networks, ComputerStomach NeoplasmsAgedConvolutional Neural NetworksFemaleHumansMaleMiddle AgedProspective StudiesSensitivity and SpecificityVideo RecordingAccuracyDeep learningEndoscopy, gastrointestinal, diagnostic rateStomach neoplasms

Identifiers

PMID39843600

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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.