Evidence map›Paper›PMID 39407081›Full record

ArticleDigestive diseases and sciences2024

A Multi-task Neural Network for Image Recognition in Magnetically Controlled Capsule Endoscopy.

Ting Xu, Yuan-Yi Li, Fang Huang, Min Gao, Can Cai, Song He, Zhi-Xuan Wu

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In one paragraph

Article in Digestive diseases and sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

  1. Review
  2. [Magnetically Controlled Capsule Endoscopy for Upper Gastrointestinal Examination].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2026
    Review
  3. 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

7 authors.

Ting Xu *Department of Gastroenterology, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China.
Yuan-Yi Li *The Bioengineering College of Chongqing University, Chongqing, China.
Fang HuangChongqing Jinshan Technology (Group) Co., Ltd., Chongqing, China.
Min GaoChongqing Jinshan Technology (Group) Co., Ltd., Chongqing, China.
Can CaiDepartment of Gastroenterology, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China.
Song HeDepartment of Gastroenterology, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China.
Zhi-Xuan WuDepartment of Gastroenterology, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China. xuan@hospital.cqmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsPhysicians are required to spend a significant amount of reading time of magnetically controlled capsule endoscopy. However, current deep learning models are limited to completing a single recognition task and cannot replicate the diagnostic process of a physician. This study aims to construct a multi-task model that can simultaneously recognize gastric anatomical sites and gastric lesions.

methodsA multi-task recognition model named Mul-Recog-Model was established. The capsule endoscopy image data from 886 patients were selected to construct a training set and a test set for training and testing the model. Based on the same test set, the model in this study was compared with the current single-task recognition model with good performance.

resultsThe sensitivity and specificity of the model for recognizing gastric anatomical sites were 99.8% (95% confidence intervals: 99.7-99.8) and 98.5% (95% confidence intervals: 98.3-98.7), and for gastric lesions were 98.8% (95% confidence intervals: 98.3-99.2) and 99.4% (95% confidence intervals: 99.1-99.7). Moreover, the positive predictive value, negative predictive value, and accuracy of the model were more than 95% in recognizing gastric anatomical sites and gastric lesions. Compared with the current single-task recognition model, our model showed comparable sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (p < 0.01, except for the negative predictive value of ResNet, p > 0.05). The Areas Under Curve of our model were 0.985 and 0.989 in recognizing gastric anatomical sites and gastric lesions. Furthermore, the model had 49.1 M parameters and 38.1G Float calculations. The model took 15.5 ms to recognize an image, which was less than the superposition of multiple single models (p < 0.01).

conclusionsThe Mul-Recog-Model exhibited high sensitivity, specificity, PPV, NPV, and accuracy. The model demonstrated excellent performance in terms of parameters quantity, Float computation, and computing time. The utilization of the model for recognizing gastric images can improve the efficiency of physicians' reports and meet complex diagnostic requirements.

Indexed as

Capsule EndoscopyAdultAgedDeep LearningFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedNeural Networks, ComputerPredictive Value of TestsSensitivity and SpecificityStomachArtificial intelligenceGastric anatomical sitesGastric lesionsMulti-task neural network

Identifiers

PMID39407081

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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.