Predicting Histological Healing and Recurrence in Ulcerative Colitis by Assessing Mucosal Vascular Pattern Under Narrow-Band Imaging Endoscopy

He, Tong‐Chuan; Zong, Li; Pan, Peng; Sun, Shanming; Qu, Hongmei

doi:10.3389/fmed.2022.869981

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…White-light imaging can assess the surface structure and vessel pattern of the mucosa, but it does not sufficiently evaluate the inflammatory infiltrate in the lamina propria. This suggests that endoscopy may underestimate the degree of inflammation in UC, 41 , 42 which can, in turn, limit the congruence between histological and endoscopic inflammatory activity. Presently, MES and UCEIS are the primary endoscopic scoring systems for mucosal inflammation in clinical practice, yet neither aligns perfectly with histological inflammation.…”

Section: Discussionmentioning

confidence: 99%

Application of deep learning in the diagnosis and evaluation of ulcerative colitis disease severity

Jiang,

Luo,

Nan

et al. 2023

Therap Adv Gastroenterol

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Background: Achieving endoscopic and histological remission is a critical treatment objective in ulcerative colitis (UC). Nevertheless, interobserver variability can significantly impact overall assessment performance. Objectives: We aimed to develop a deep learning algorithm for the real-time and objective evaluation of endoscopic disease activity and prediction of histological remission in UC. Design: This is a retrospective diagnostic study. Methods: Two convolutional neural network (CNN) models were constructed and trained using 12,257 endoscopic images and biopsy results sourced from 1124 UC patients who underwent colonoscopy at a single center from January 2018 to December 2022. Mayo Endoscopy Subscore (MES) and UC Endoscopic Index of Severity Score (UCEIS) assessments were conducted by two experienced and independent reviewers. Model performance was evaluated in terms of accuracy, sensitivity, and positive predictive value. The output of the CNN models was also compared with the corresponding histological results to assess histological remission prediction performance. Results: The MES-CNN model achieved 97.04% accuracy in diagnosing endoscopic remission of UC, while the MES-CNN and UCEIS-CNN models achieved 90.15% and 85.29% accuracy, respectively, in evaluating endoscopic severity of UC. For predicting histological remission, the CNN models achieved accuracy and kappa values of 91.28% and 0.826, respectively, attaining higher accuracy than human endoscopists (87.69%). Conclusion: The proposed artificial intelligence model, based on MES and UCEIS evaluations from expert gastroenterologists, offered precise assessment of inflammation in UC endoscopic images and reliably predicted histological remission.

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Section: Discussionmentioning

confidence: 99%