ArticleTechniques in coloproctology2026
Development of a convolutional neural network for the endoscopic classification of pouchitis in patients after restorative proctocolectomy.
Article in Techniques in coloproctology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04864587 (Artificial Intelligence in Image Recognition of Pouchoscopies in Patients With Restorative Proctocolectomy), which is not on this map. Not yet cited in PubMed.
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Artificial Intelligence in Image Recognition of Pouchoscopies in Patients With Restorative Proctocolectomy
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6 authors.
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Abstract
BACKGROUND AND
aimsThe aim of this prospective single-center study is to train convolutional neural networks (CNNs) to detect the presence of pouchitis in two-dimensional (2D) images acquired during pouchoscopies and test its feasibility.
methodsTwo separate networks were constructed. The goal of network 1 was to detect whether an inflammation was present. Network 2 was designed to classify endoscopic findings of pouchitis, according to the pouchitis disease activity index (PDAI) score. The dataset was divided into three distinct sets: a training set, a validation set, and a test set. The performance was quantified using a tenfold cross-validation approach.
resultsFor the detection of inflammation, sensitivity was 71.78% with a specificity of 90.35%. When differentiating the six endoscopic findings according to the PDAI score, the sensitivity ranged from a low of 38% for the 'ulceration' class to a high of 67.18% for the 'friability' class, with a specifity of 94.12% ('ulceration') and 96.57% ('friability').
conclusionsThis study shows that an artificial, intelligence-based image recognition software can be trained to recognize the endoscopic features of pouchitis with reasonable accuracy. The results, although encouraging, confirm that artificial intelligence (AI) performance in this context remains below human expert level. A larger dataset, human benchmarking and more appropriate endoscopic markers are required to reach clinically relevant performance. Trial registration This trial was registered in the 'ClinicalTrials.gov' database on 26 April 2021 (NCT04864587).
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