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.