ArticleSurgical endoscopy2026
Application of artificial intelligence for detection of Helicobacter pylori infection by magnetically controlled capsule endoscopy.
Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed.
- AI-Supported Prediction of Vesicoureteral Reflux in Children Based on Cystoscopic Configuration of the Ureteric Orifice.Journal of clinical medicine · 2026Article
- [Magnetically Controlled Capsule Endoscopy for Upper Gastrointestinal Examination].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundApplication of artificial intelligence (AI) in magnetically controlled capsule endoscopy (MCCE) is increasing. The aim of this study was to develop an AI system that can automatically detect Helicobacter pylori (H. pylori) infection in MCCE images and to evaluate the diagnostic performance of the system.
methodsThis study prospectively enrolled subjects with known H. pylori infection status for MCCE examination between April 2022 and March 2023. The MCCE images were collected and prepared for the data set (80% for training, 10% for validation and 10% for testing). Four convolutional neural network models (i.e., MobileNetV2, DenseNet264, ShuffleNetV2 and ResNet50) were applied in the AI system respectively. We evaluated their diagnostic performance and identify the optimal model by calculating the accuracy, sensitivity, specificity, and average reading time for per image.
resultsA total of 142 subjects were registered including 71 H. pylori-positive and 71 H. pylori-negative. The numbers of images in the training set, validation set and testing set were 25,985 (114 patients), 2767 (14 patients), and 3027 (14 patients), respectively. The accuracy, sensitivity, specificity, and average reading time for per image of each CNN model were as follows: MobileNetV2 model, 95.7, 98.1, 93.3% and 0.01176 s; DenseNet264 model, 95.3, 98.5, 92.2% and 0.04572 s; ShuffleNetV2 model, 95.7, 98.5, 93.1% and 0.00588 s; ResNet50 model, 95.1, 95.8, 94.5% and 0.01110 s, respectively.
conclusionsThe AI system based on the ShuffleNetV2 model achieved better performance, highlighting its potential for future application to help clinicians detect H. pylori infection in MCCE examination.
Indexed as
Identifiers
41295999What Socratic holds
Registered trials
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 Socratic graph.