The role and importance of endoscopic mucosal healing in ulcerative colitis

Osterman, Mark T.

doi:10.1016/j.tgie.2004.09.002

Cited by 2 publications

(4 citation statements)

References 84 publications

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“…Presently, MES and UCEIS are the primary endoscopic scoring systems for mucosal inflammation in clinical practice, yet neither aligns perfectly with histological inflammation. 5 , 43 Score systems that show higher concordance with histological severity are in development. The capability of AI to identify and analyze details that may be missed by clinicians highlights its potential role in improving the correlation between endoscopic scores and histological inflammation and in predicting histological remission.…”

Section: Discussionmentioning

confidence: 99%

“…22 The MES evaluation criteria range from 0 to 3: MES = 0 (MES 0) indicates the absence of obvious active lesions; MES = 1 (MES 1) indicates mild lesions, with endoscopic features of erythema and reduced blood vessel texture; MES = 2 (MES 2) indicates moderate lesions, with endoscopic features of obvious erythema, blood vessels, texture loss, and erosion; and MES = 3 (MES 3) indicates severe lesions, with endoscopic features of spontaneous bleeding and ulcer formation. 8 The UCEIS scoring system ranges from 0 to 8 and consists of three descriptors (calculated as a simple sum): vascular pattern (scored as 0-2), bleeding (scored as 0-3), and erosions and ulcers (scored as 0-3), which are further stratified into four grades: that is, remission (0), mild (1-3), moderate (4)(5)(6), and severe (7)(8). 9 For this study, images were evaluated based on MES and UCEIS endoscopic severity by two expert gastroenterologists, QN and YM, both with over 10 years of experience.…”

Section: Data Collectionmentioning

confidence: 99%

“…Treatment choice depends on disease severity, with mild to moderately active UC usually treated with oral/topical 5-aminosalicylic acid or oral glucocorticoids and severe UC usually requiring intravenous glucocorticoid therapy, even immunosuppressants, and expensive biological reagents. 5 , 6 Assessment of UC patients and their response to therapy primarily involves colonoscopy and histological analysis. Several metrics have been proposed to evaluate the endoscopic activity of UC, 7 including the Mayo Endoscopic Subscore (MES) 8 and Ulcerative Colitis Endoscopic Index of Severity (UCEIS).…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Mild to moderately active UC is usually treated with oral/topical 5-aminosalicylic acid or oral glucocorticoids. Severely active patients usually need to be admitted to the hospital for intravenous glucocorticoid therapy, even received immunosuppressants and expensive biological reagents 5,6 . In addition to clinical evaluation, complete blood count testing, erythrocyte sedimentation rate testing, and C-reactive protein testing, the severity of UC is mainly assessed by colonoscopy and histological slice.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithm for Diagnose Endoscopic and Histological Images With Ulcerative Colitis

Yan

Luo

Nan

et al. 2022

Preprint

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The goal of treatment for ulcerative colitis is to achieve histological and endoscopic remission. Aiming at the problem that the observer will be affected by subjective factors in the endoscopic evaluation of ulcerative colitis and the cumbersome diagnosis process of histological images, this paper aims to develop a computer-assisted diagnosis system for real-time, objective diagnosis of endoscopic images and use the trained CNN model to predict histological images of patients with ulcerative colitis. Diagnosing endoscopic remission of ulcerative colitis, the accuracy of the CNN is 97.04% (95% CI,96.26%:97.62%). Diagnosing the severity of endoscopic inflammation in patients with ulcerative colitis, the accuracy of the CNN is 90.15% (95% CI, 89.49%:90.82%). The accuracy of predicting histological remission was 91.28%. The kappa coefficient between the CNN model and the biopsy results was 82.56%. The proposed computer-aided diagnosis system can effectively evaluate the inflammation of endoscopic images of patients with ulcerative colitis and predict the remission of histological images with high accuracy and consistency.

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The role and importance of endoscopic mucosal healing in ulcerative colitis

Cited by 2 publications

References 84 publications

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

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

Deep Learning Algorithm for Diagnose Endoscopic and Histological Images With Ulcerative Colitis

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