ArticleiGIE : innovation, investigation and insights2025
Development of an artificial intelligence model to identify duodenal polyps in patients with familial adenomatous polyposis.
Article in iGIE : innovation, investigation and insights, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Precancerous duodenal polyps can be subtle in familial adenomatous polyposis (FAP). Artificial intelligence models can detect gastrointestinal (GI) tract pathology; thus, we aimed to develop a model to identify duodenal polyps in FAP patients. Methods: Images of duodenal polyps from upper GI endoscopic surveillance in FAP patients were obtained. Polyps were manually annotated (Label Studio; HumanSignal, San Francisco, Calif, USA), and the nnU-Net framework (Applied Computer Vision Lab of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center, Heidelberg, Germany) was used to automate polyp segmentation. Images were randomly divided into training, validation, and test sets (80%, 10%, and 10%, respectively). Primary performance metric was Dice coefficient (score 0-1). Manual counting was also used to compare the model's ability to identify polyps. Results: Mean polyps per image by manual count was 4.2 (standard deviation [SD] = 4.8) and by prediction model 4.0 (SD = 4.7). Sixty of 87 images (69.0%) had 0 missed polyps, 74 of 87 (85.1%) missed a maximum of 1 polyp, and 83 of 87 (95.4%) missed a maximum of 4. Mean missed polyps per image was 0.9 (SD = 2.3), and the model identified 287 of 365 (78.6%) polyps. Forty of 87 (46.0%) had falsely identified polyps (mean 0.8; SD = 1.0). Missed polyps were smaller than identified polyps (mean 73.3 [SD = 45.3] versus 233.5 [SD = 274.5] pixel diameter, respectively). There was a complete match (no missed or false positive) in 33 of 87 images (37.9%). Dice coefficient was 0.73. Conclusions: A model to identify duodenal polyps in FAP was successfully created. Although the Dice coefficient is modest compared with that of colon polyp models, duodenal anatomy creates a challenging background for human and computer detection. Rate of polyp detection, likely a superior marker of goal achievement, was >75%, with a low false polyp rate (mean <1/image). This prototype model is the first step toward a refined algorithm to assist in identification of duodenal polyps with a need for larger prospective studies.
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.