ArticleCancer science2022
Development and multi-institutional validation of an artificial intelligence-based diagnostic system for gastric biopsy.
Article in Cancer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- AI caption generation model for digital pathology of adenocarcinoma in endoscopic histopathology using multi-instance attention mechanisms.Scientific reports · 2026Article
- Enhancing Inter-institutional AI Research Collaboration Across Multidisciplinary Teams to Increase the National AI Workforce in Healthcare.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
- Development and multi-institutional validation of an artificial intelligence-based diagnostic system for gastric biopsy.Cancer science · 2022Article
Corrections and comments
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Authors and funding
17 authors at 6 institutions in 1 country.
Funding
Abstract
To overcome the increasing burden on pathologists in diagnosing gastric biopsies, we developed an artificial intelligence-based system for the pathological diagnosis of gastric biopsies (AI-G), which is expected to work well in daily clinical practice in multiple institutes. The multistage semantic segmentation for pathology (MSP) method utilizes the distribution of feature values extracted from patches of whole-slide images (WSI) like pathologists' "low-power view" information of microscopy. The training dataset included WSIs of 4511 gastric biopsy tissues from 984 patients. In tissue-level validation, MSP AI-G showed better accuracy (91.0%) than that of conventional patch-based AI-G (PB AI-G) (89.8%). Importantly, MSP AI-G unanimously achieved higher accuracy rates (0.946 ± 0.023) than PB AI-G (0.861 ± 0.078) in tissue-level analysis, when applied to the cohorts of 10 different institutes (3450 samples of 1772 patients in all institutes, 198-555 samples of 143-206 patients in each institute). MSP AI-G had high diagnostic accuracy and robustness in multi-institutions. When pathologists selectively review specimens in which pathologist's diagnosis and AI prediction are discordant, the requirement of a secondary review process is significantly less compared with reviewing all specimens by another pathologist.
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Registered trials
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