An AI tool can distinguish histological remission from activity in biopsies of UC and predict flare-ups -- and could have a role in both clinical trials and routine practice.
They used 535 digitized biopsies from 273 patients with UC to develop and test the tool. They used a subset of 118 to train it to distinguish remission from activity, 42 to calibrate it, and 375 to test it. An additional 154 biopsies from 58 patients with UC were used to externally validate the tool.
The AI tool had"strong diagnostic performance to detect disease activity" with an overall area under the receiver operating characteristic curve of 0.87 and sensitivity and specificity of 89% and 85%, respectively. The AI tool could also predict the presence of endoscopic inflammation in the biopsy area with about 80% accuracy.
The hazard ratios for disease flare-up between the AI system and pathologists assessed by PHRI was similar ,"demonstrating the ability of the computer to stratify the risk of flare comparably well to pathologists," they add. The AI system delivered results in an average of 9.8 seconds per slide.
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