ArticleEndoscopy international open2026
Development and validation of an artificial intelligence classifier for histologic grading of superficial non-ampullary duodenal epithelial tumors.
Article in Endoscopy international open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and study aims: Superficial non-ampullary duodenal epithelial tumors (SNADETs) are being increasingly diagnosed, and accurate endoscopic diagnosis is essential for determining appropriate treatment strategies. However, no standardized diagnostic method currently exists. We developed a deep learning-based artificial intelligence (AI) classifier to distinguish between low-grade and high-grade neoplasia and evaluated its diagnostic performance. Patients and methods: The training set of 85 SNADET cases (low-grade neoplasia, n = 41; high-grade neoplasia, n = 44; white-light images, n = 317) from Yamaguchi University were used to train the AI classifier. Validation was performed using a test set of 69 lesions (low-grade neoplasia, n = 31; high-grade neoplasia, n =38; images, n = 229) from three institutions. Three expert endoscopists also evaluated the test set using an endoscopic scoring system for comparison. Results: The AI classifier achieved 78.3% accuracy, 92.1% sensitivity, 61.3% specificity, 74.5% positive predictive value (PPV), 86.4% negative predictive value (NPV), and an F1 measure of 0.824. In contrast, endoscopist diagnosis yielded 58.0% accuracy, 50.0% sensitivity, 67.7% specificity, 65.5% PPV, 52.5% NPV, and an F1 measure of 0.567. Diagnostic accuracy and sensitivity of the AI was significantly higher than that of the endoscopists ( Conclusions: The AI classifier demonstrated higher sensitivity and overall diagnostic performance in comparison to endoscopists in differentiating high-grade neoplasia, suggesting its potential utility in guiding treatment decisions.
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.