Quantitative assessment of mucosal architecture using computer-based analysis of confocal laser endomicroscopy in inflammatory bowel diseases
Open accessAbstract
BACKGROUND AND AIMS: Confocal laser endomicroscopy (CLE) might discriminate mucosal lesions between Crohn's disease (CD) and ulcerative colitis (UC). However, the analysis of CLE images requires time-consuming methods, a long training time, and potential impediments, such as significant interobserver variability. Therefore, we developed a computer-based method to analyze mucosal architecture from CLE images and discriminate between healthy subjects and patients with inflammatory bowel disease (IBD) as well as between UC and CD patients. METHODS: We retrospectively screened patients who had undergone CLE either for an evaluation of IBD in remission or for colorectal cancer screening (control subjects) between 2009 and 2016. We assessed 14 morphologic and functional parameters in each CLE recording from 23 CD patients, 27 UC patients, and 9 control patients. Next, we constructed 2 scores, 1 for the IBD diagnosis and 1 for the differential diagnosis between UC and CD. RESULTS: In IBD patients, the mean intercrypt distance, wall thickness, and fluorescein leakage through the colonic mucosa were significantly increased compared with control patients by 155%, 188%, and 297%, respectively (P < .05). In UC patients, the same parameters were significantly increased by 109%, 117%, and 174%, respectively (P < .05), compared with CD patients. IBD diagnosis had 100% (95%CI, 93%; 100%) sensitivity and 100% (95%CI, 66%; 100%) specificity. IBD differential diagnosis provided discrimination of UC from CD patients with 92% (95%CI, 75%; 99%) sensitivity and 91% (95%CI, 72%; 99%) specificity. CONCLUSIONS: Confirming these results using prospective validation cohorts can substantiate that computer-based analysis of CLE images may provide new biomarkers for the diagnosis and characterization of IBD.
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Several AI algorithms have been developed to accurately grade disease activity across various endoscopic scoring systems in ulcerative colitis (UC), showing excellent diagnostic performance and strong concordance with experts.
“Recently, several AI algorithms have been developed to accurately grade disease activity across various endoscopic scoring systems in ulcerative colitis (UC) to overcome these limitations, showing excellent diagnostic performance and strong concordance with experts.13-23”
From Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease (2024) · cites this paper as [22]
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- Authors
- Quénéhervé, L., David, G., Bourreille, A., Hardouin, J. B., Rahmi, G., Neunlist, M., Brégeon, J., & Coron, E.
- Affiliations
- Inserm (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Corresponding author
- Coron, Emmanuel
- ORCID
- Venue
- Gastrointestinal Endoscopy
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 89
- Issue
- 3
- Pages
- 626-636
- ISSN
- 0016-5107 (ISSN-L)
- OpenAlex
- W2886782721
- Cited by
- 68
- References
- 29
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 10 · 2023: 9 · 2022: 9 · 2021: 9 · 2020: 7 · 2019: 5 · 2018: 1
- FWCI
- 3.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Gastrointestinal Endoscopy (acceptedVersion, other-oa)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Inflammatory Bowel Disease
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- confocal laser endomicroscopy, inflammatory bowel disease, Crohn's disease, ulcerative colitis, computer-based image analysis, mucosal architecture, differential diagnosis, fluorescein leakage, colorectal cancer screening
- MeSH terms
- Adolescent, Adult, Aftercare, Aged, Colitis, Ulcerative, Colon, Colonoscopy, Contrast Media, Crohn Disease, Diagnosis, Differential, Female, Humans, Image Processing, Computer-Assisted, Intestinal Mucosa, Male, Middle Aged, Permeability, Retrospective Studies, Sensitivity and Specificity, Colorectal Neoplasms, Inflammatory Bowel Diseases, Multivariate Analysis, Logistic Models, Microscopy, Confocal, Fluorescein, Early Detection of Cancer, Young Adult
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