Artificial intelligence-assisted analysis of pan-enteric capsule endoscopy in patients with suspected Crohn's disease. A study on diagnostic performance
Open accessAbstract
Background and Aim: Pan-enteric capsule endoscopy [PCE] is a highly sensitive but time-consuming tool for detecting pathology. Artificial intelligence [AI] algorithms might offer a possibility to assist in the review and reduce the analysis time of PCE. This study examines the agreement between PCE assessments aided by AI technology and standard evaluations, in patients suspected of Crohn’s disease [CD]. Method: PCEs from a prospective, blinded, multicentre study, including patients suspected of CD, were processed by the deep learning solution AXARO® [Augmented Endoscopy, Paris, France]. Based on the image output, two observers classified the patient’s PCE as normal or suggestive of CD, ulcerative colitis, or cancer. The primary outcome was per-patient sensitivities and specificities for detecting CD and inflammatory bowel disease [IBD]. Complete reading of PCE served as the reference standard. Results: A total of 131 patients’ PCEs were analysed, with a median recording time of 303 min. The AXARO® framework reduced output to a median of 470 images [2.1%] per patient, and the pooled median review time was 3.2 min per patient. For detecting CD, the observers had a sensitivity of 96% and 92% and a specificity of 93% and 90%, respectively. For the detection of IBD, both observers had a sensitivity of 97% and had a specificity of 91% and 90%, respectively. The negative predictive value was 95% for CD and 97% for IBD. Conclusions: Using the AXARO® framework reduced the initial review time substantially while maintaining high diagnostic accuracy—suggesting its use as a rapid tool to rule out IBD in PCEs of patients suspected of Crohn’s disease.
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AI-enabled accurate disease assessment and scoring can potentially lead to a reduction in the necessary sample size for clinical trials.
“Additionally, AI-enabled accurate disease assessment and scoring can potentially lead to a reduction in the necessary sample size for clinical trials.26”
From Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease (2024) · cites this paper as [26]
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- Authors
- Brodersen, J. B., Jensen, M. D., Leenhardt, R., Kjeldsen, J., Histace, A., Knudsen, T., & Dray, X.
- Affiliations
- University of Southern Denmark (DK)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Brodersen, Jacob Broder
- ORCID
- Venue
- Journal of Crohn's and Colitis
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-08-01
- Volume
- 18
- Issue
- 1
- Pages
- 75-81
- ISSN
- 1873-9946 (ISSN-L)
- 1876-4479
- OpenAlex
- W4385449513
- Cited by
- 55
- References
- 32
- Citations by year
- 2026: 18 · 2025: 22 · 2024: 14 · 2023: 1
- FWCI
- 15.75
- Type
- journal article
- Language
- en
- Open access
- Locations
- Journal of Crohn s and Colitis (publishedVersion)
- PubMed (publishedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, other-oa)
- HAL (submittedVersion)
- Funders
- Region of Southern Denmark (16/9780, 19/37100)
- Danish Colitis and Crohn’s Association
- Topics
- Health Sciences › Medicine › Gastroenterology › Gastrointestinal Bleeding Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Inflammatory Bowel Disease
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Keywords
- Crohn's disease
- MeSH terms
- Artificial Intelligence, Crohn Disease, Humans, Prospective Studies, Inflammatory Bowel Diseases, Capsule Endoscopy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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