SynthesisJournal of medical Internet research2020
Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.
Synthesis in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 7 of them syntheses that pooled 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.
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
50 citing papers in PubMed, 7 syntheses or guidelines pooled it, 98 citations in OpenAlex.
- The Diagnostic Performance of Linked Color Imaging Compared to White Light Imaging in Endoscopic Diagnosis ofGut and liver · 2024Pooled it
- Pooled it
- Novel endoscopic techniques for the diagnosis of gastricFrontiers in microbiology · 2024Pooled it
- The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study.Journal of medical Internet research · 2022Pooled it
- Predictive Role of Neutrophil to Lymphocyte Ratio in Adnexal Torsion: A Systematic Review and Meta-Analysis.Mediators of inflammation · 2022Pooled it
- Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis.Journal of medical Internet research · 2021Pooled it
- Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy.Journal of medical Internet research · 2021Pooled it
- Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study.BMC gastroenterology · 2024Trial
- Application value of an artificial intelligence-based diagnosis and recognition system in gastroscopy training for graduate students in gastroenterology: a preliminary study.Wiener medizinische Wochenschrift (1946) · 2024Trial
- Clinical feasibility of deep learning-assisted classification of Helicobacter pylori infection in endoscopic imagery: a reader study.The Korean journal of internal medicine · 2026Article
- Diagnostic accuracy of image-based deep learning for glaucomatous optic neuropathy detection: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Automated diagnosis of Helicobacter pylori infection from routine endoscopic images using deep learning: a development and validation study.BMC gastroenterology · 2026Article
- Application of artificial intelligence for detection of Helicobacter pylori infection by magnetically controlled capsule endoscopy.Surgical endoscopy · 2026Article
- Limited discriminative performance of endoscopic deep learning forFrontiers in gastroenterology (Lausanne, Switzerland) · 2026Article
- The role of artificial intelligence in gastroenterology: current perspectives and future directions-narrative review.Translational gastroenterology and hepatology · 2026Review
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Artificial intelligence-assisted endoscopic diagnosis system for diagnosing Helicobacter pylori infection: a multicenter study.BMC medicine · 2025Article
- Role of artificial intelligence in gastric diseases.World journal of gastroenterology · 2025Review
- Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases.World journal of clinical oncology · 2025Review
- AI-assisted diagnosis of Helicobacter pylori infection: strengths, limitations, and future directions.Journal of gastroenterology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHelicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification.
objectiveThis study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images.
methodsTwo independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed.
resultsUltimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images.
conclusionsAn AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome.
trial registrationPROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.