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Automatic, computer-aided determination of endoscopic and histological inflammation in patients with mild to moderate ulcerative colitis based on red density


Background The objective evaluation of endoscopic disease activity is key in ulcerative colitis (UC). A composite of endoscopic and histological factors is the goal in UC treatment. We aimed to develop an operator-independent computer-based tool to determine UC activity based on endoscopic images.

Methods First, we built a computer algorithm using data from 29 consecutive patients with UC and 6 healthy controls (construction cohort). The algorithm (red density: RD) was based on the red channel of the red-green-blue pixel values and pattern recognition from endoscopic images. The algorithm was refined in sequential steps to optimise correlation with endoscopic and histological disease activity. In a second phase, the operating properties were tested in patients with UC flares requiring treatment escalation. To validate the algorithm, we tested the correlation between RD score and clinical, endoscopic and histological features in a validation cohort.

Results We constructed the algorithm based on the integration of pixel colour data from the redness colour map along with vascular pattern detection. These data were linked with Robarts histological index (RHI) in a multiple regression analysis. In the construction cohort, RD correlated with RHI (r=0.74, p<0.0001), Mayo endoscopic subscores (r=0.76, p<0.0001) and UC Endoscopic Index of Severity scores (r=0.74, p<0.0001). The RD sensitivity to change had a standardised effect size of 1.16. In the validation set, RD correlated with RHI (r=0.65, p=0.00002).

Conclusions RD provides an objective computer-based score that accurately assesses disease activity in UC. In a validation study, RD correlated with endoscopic and histological disease activity.

  • artificial intelligence
  • evaluation
  • response to treatment
  • remission
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