Background Ulcerative colitis is a remitting and relapsing chronic inflammatory bowel disease . To prevent complications, UC treatment aims to eliminate inflammation, with histopathology being the most precise way for detecting inflammation and distinguishing it from remission. Histologic remission is associated with better clinical results and has thus become a therapy objective.
From September 2016 to November 2019, the team recruited patients from 11 centers for the primary analysis. The eligible participants had confirmed UC for over a year, irrespective of disease activity and a colonoscopy indication. A minimum of two targeted tissue specimens were obtained from the most typical sites of healing or inflammation present in the rectum and sigmoid, which were the same regions where the endoscopic evaluation was recorded.
Picasso Histologic remission index was implemented within the CAD system by devising a framework constructed according to multiple-instance learning with restrictions. The final whole-biopsy prediction was derived using a multiple-instance learning strategy that assessed the examination of each biopsy patch and combined the assessment into a final result, activity, or remission as per PHRI.
The same method trained to identify neutrophils and predict PHRI was then compared to human evaluations of remission/activity based on the Robart Histopathology index and Nancy Histological index . The team observed that the system distinguished histological remission and activity with 94% sensitivity, 76% specificity, 53% PPV, 98% NPV, 80% accuracy, and an AUROC of 85%.
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