Development of New Diagnostic Tools in Capsule Endoscopy
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Bowel Disease, Crohn Disease, Celiac Disease, Chronic Diarrhea. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- France
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
Patients participating to this study will provide images and videos of capsule endoscopy to train, tune and evaluate technological bricks of artificial intelligence solutions, in order to improve diagnostic performances of the procedure, while reducing reading time by physicians.
Detailed description
Capsule endoscopy is a minimally-invasive diagnostic procedure based on the ingestion (or endoscopic delivery) of a miniaturized biocompatible, camera. Capsules capture tenths of thousands images of the digestive tract. Reading the captured images and reporting is long, tedious, and at risk of errors when the reader's attention is disturbed. Artificial intelligence is expected to alleviate these limitations, by both improving diagnostic performances of capsule endoscopy while reducing reading time.
Any patient in whom a capsule endoscopy examination is performed as part of routine care will be invited to participate to the study. Their de-identified images and videos will be extracted, thus allowing the creation of several databases for training, tuning and testing technological bricks of artificial intelligence. Basic clinical data will be collected (age, gender, indication for capsule endoscopy, type of device, ingestion or delivery of capsule). Images and videos will be characterized centrally and consensually by a panel of 3 expert readers, according to their level of relevance in relation to the type and indication of capsule endoscopy.
The various, developed technological bricks will aim to automatically detect and characterize anatomical landmarks and abnormal findings, and to quote the intestine cleanliness.
Assessment of diagnostic performance and reading time will be performed within a few months or up to five years, for each technological brick, individually and then combined, according to their stepwise development.
Primary outcome measures
- Develop an artificial intelligence solution to help with diagnosis in VCE [Time frame: Through study completion, 5 years]
Secondary outcome measures (4)
- Evaluation of the diagnostic performance of the A.I. solution in terms of detection [Time frame: Through study completion, 5 years]
- Evaluation of the diagnostic performance of the A.I. solution in terms of characterisation [Time frame: Through study completion, 5 years]
- Evaluation of the diagnostic performance of the A.I. solution in terms of recognition of anatomical landmarks [Time frame: Through study completion, 5 years]
- Evaluation of the diagnostic performance of the A.I. solution in terms of quality of preparation of the various segments of the digestive tract [Time frame: Through study completion, 5 years]
Eligibility criteria
Inclusion criteria
\- Any patient in whom a capsule endoscopy examination is performed as part of routine care.
Exclusion criteria
\- Opposition to the use of images and videos from daily, routine care for research purposes.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
France · 1 center
- Centre d'Endoscopie Digestive Hôpital Saint-Antoine — Paris
Identifiers
NCT: NCT06152289 · APHP210791