Clinical trial NCT06152289: Development of New Diagnostic Tools in Capsule Endoscopy
Official title: Development of New Diagnostic Tools in Capsule Endoscopy
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Bowel Disease
Crohn Disease
Celiac Disease
Chronic Diarrhea
Tumor
Keywords: Small bowel
Capsule endoscopy
Artificial intelligence
Lead sponsor: Assistance Publique - Hôpitaux de Paris
Sponsor class: OTHER
First posted: 2023-11-30
Enrollment: 10000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Any patient in whom a capsule endoscopy examination is performed as part of routine care

Brief summary: 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.

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.

Primary outcomes:
measure: Develop an artificial intelligence solution to help with diagnosis in VCE
timeFrame: Through study completion, 5 years
Secondary outcomes:
measure: Evaluation of the diagnostic performance of the A.I. solution in terms of detection
timeFrame: Through study completion, 5 years
measure: Evaluation of the diagnostic performance of the A.I. solution in terms of characterisation
timeFrame: Through study completion, 5 years
measure: Evaluation of the diagnostic performance of the A.I. solution in terms of recognition of anatomical landmarks
timeFrame: Through study completion, 5 years
measure: Evaluation of the diagnostic performance of the A.I. solution in terms of quality of preparation of the various segments of the digestive tract
timeFrame: Through study completion, 5 years
Other outcomes:
Locations:
city: Paris
country: France
facility: Centre d'Endoscopie Digestive Hôpital Saint-Antoine
Study officials:
References: