ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2020
Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: a single-center prospective study (with video).
Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- The Diagnostic Performance of Linked Color Imaging Compared to White Light Imaging in Endoscopic Diagnosis ofGut and liver · 2024Pooled it
- Novel endoscopic techniques for the diagnosis of gastricFrontiers in microbiology · 2024Pooled it
- Artificial intelligence in gastric cancer: a systematic review.Journal of cancer research and clinical oncology · 2020Pooled it
- Article
- Automated diagnosis of Helicobacter pylori infection from routine endoscopic images using deep learning: a development and validation study.BMC gastroenterology · 2026Article
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Artificial intelligence in gastric cancer research: a bibliometric and visualized analysis from 1993 to 2026.Frontiers in oncology · 2026Review
- Artificial intelligence-assisted endoscopic diagnosis system for diagnosing Helicobacter pylori infection: a multicenter study.BMC medicine · 2025Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Multistage deep learning for classification of Helicobacter pylori infection status using endoscopic images.Journal of gastroenterology · 2025Article
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- AI-powered three-categoryFrontiers in microbiology · 2025Article
- Artificial intelligence in advanced endoscopic imaging: transforming optical diagnosis in gastroenterology.Frontiers in medicine · 2025Review
- Advancing precise diagnosis of nasopharyngeal carcinoma through endoscopy-based radiomics analysis.iScience · 2024Article
- Endoscopic appearances of gastric mucosa in different endoscopic models according to H. pylori infection status.JGH open : an open access journal of gastroenterology and hepatology · 2024Review
- Development of an Artificial Intelligence Diagnostic System Using Linked Color Imaging for Barrett's Esophagus.Journal of clinical medicine · 2024Article
- Applications of artificial intelligence in gastroscopy: a narrative review.The Journal of international medical research · 2024Review
- Clinical usefulness of linked color imaging in identifyingWorld journal of gastrointestinal endoscopy · 2023Article
- Diagnostic usefulness of deep learning methods forJGH open : an open access journal of gastroenterology and hepatology · 2023Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHelicobacter pylori (H. pylori) eradication is required to reduce incidence related to gastric cancer. Recently, it was found that even after the successful eradication of H. pylori, an increased, i.e., moderate, risk of gastric cancer persists in patients with advanced mucosal atrophy and/or intestinal metaplasia. This study aimed to develop a computer-aided diagnosis (CAD) system to classify the status of H. pylori infection of patients into three categories: uninfected (with no history of H. pylori infection), currently infected, and post-eradication.
methodsThe CAD system was based on linked color imaging (LCI) combined with deep learning (DL). First, a validation dataset was formed for the CAD systems by recording endoscopic movies of 120 subjects. Next, a training dataset of 395 subjects was prepared to enable DL. All endoscopic examinations were recorded using both LCI and white-light imaging (WLI). These endoscopic data were used to develop two different CAD systems, one for LCI (LCI-CAD) and one for WLI (WLI-CAD) images.
resultsThe diagnostic accuracy of the LCI-CAD system was 84.2% for uninfected, 82.5% for currently infected, and 79.2% for post-eradication status. Comparisons revealed superior accuracy of diagnoses based on LCI-CAD data relative based on WLI-CAD for uninfected, currently infected, and post-eradication cases. Furthermore, the LCI-CAD system demonstrated comparable diagnostic accuracy to that of experienced endoscopists with the validation data set of LCI.
conclusionsThe results of this study suggest the feasibility of an innovative gastric cancer screening program to determine cancer risk in individual subjects based on LCI-CAD.
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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.