Development of an Automated Tool for the Estimation of Histological Remission in Ulcerative Colitis Using Single-Wavelength Endoscopy Technology

Abstract

Background and Aims: Ulcerative colitis (UC) management employs a strategy targeting histological and endoscopic remission. Correlation of white light endoscopy (WLE) scores with histological activity is limited. Single-wavelength endoscopy (SWE), addressing microvascular changes reflecting histological disease activity, may better assess histological remission. Our goal was to assess the accuracy of a computer-aided diagnosis (CAD) system for histological activity estimation in UC, based on either WLE or SWE. Methods: We collected 6926 sets of corresponding WLE and SWE frames in 112 patients with UC, using a prototype endoscopic system enabling both imaging methods (FUJIFILM, Tokyo, Japan). Histological remission (Geboes score ≤ 2B.0) assessed at the location of imaging was annotated for all frames, and separate WLE-CAD and SWE-CAD models were trained using deep learning for automated detection of histological remission with either imaging modality. Results: Initial training of both models on the same subset of 42 patients resulted in SWE-CAD outperforming WLE-CAD with a mean sensitivity of 88.0% vs 73.9% (p < 0.001), a mean specificity of 71.7% vs 65.6% (p = 0.45), and a diagnostic accuracy of 83.3% vs 67.5% (p < 0.005), respectively. Consecutive training of the SWE-CAD model on the entire dataset (112 patients) resulted in an accuracy of 95.2%, sensitivity of 96.4%, and specificity of 92.9% on a section level. Conclusions: By utilizing automated CAD based on non-magnifying SWE for enhanced capillary visibility vs WLE, histological remission was detected with 95.2% diagnostic accuracy in patients with UC, offering stable objectivity and helping to exclude inter-reader variability.