ArticleScientific reports2026
Artificial intelligence assistance improves endoscopist accuracy for gastric cancer dysplasia and intestinal metaplasia.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Article
- Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.BMC medical imaging · 2026Article
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7 authors.
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Abstract
Artificial intelligence (AI) can accurately classify gastric lesions, but its clinician-level impact in real-world practice remains uncertain. We compared endoscopists’ diagnostic performance with vs. without AI assistance using both still-image (M1) and video (M2) datasets. We analyzed 1,584 cases (226 cancer, 282 dysplasia, 90 non-neoplastic lesions [NNL], 649 intestinal metaplasia [IM], and 337 gastritis/normal). One representative still image per case was extracted for M1; edited five-second video clips formed M2. Six in-training endoscopists (< 3 years’ experience) independently read M1 and M2 with and without AI after a one-week washout. As a stand-alone model, AI achieved 91.31% (M1) and 92.51% (M2) accuracy for focal lesions (sensitivities 91.02% and 91.91%; specificities 95.50% and 96.12%). For IM, accuracy was 91.83% (M1) and 92.45% (M2). With AI assistance, overall reader accuracy increased from 74.92% to 86.66% in M1 (AUC 0.742 to 0.860) and likewise from 74.92% to 86.81% in M2 (AUC 0.796 to 0.900); all p < 0.05. By subtype (videos, M2), accuracy improved 80.01% to 89.85% for cancer (+ 9.84%), % 67.16%to 81.08% for dysplasia (+ 13.92%), 77.59% to 89.50% for NNL (+ 11.91%), and 68.95% to 85.34% for IM (+ 16.39%). Still-image results showed similar gains (e.g., dysplasia 67.16% to 81.32%, IM 68.95% to 79.22%, both p < 0.05). AI assistance significantly enhances endoscopists’ diagnostic accuracy across lesion types and modalities, with the largest benefits for dysplasia and IM—conditions prone to clinician-level variability. These findings suggest that AI assistance may help improve reliability and support earlier recognition of clinically significant lesions.
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