Development of New Diagnostic Tools in Capsule Endoscopy

Recruiting

• Conditions: Chronic Diarrhea; Celiac Disease; Tumor; Bowel Disease; Crohn Disease

• Registration Number: NCT06152289
• Lead Sponsor: Assistance Publique - Hôpitaux de Paris

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.

Study Timeline

• Study Start: 2023-02-10
• Study First Submitted: 2023-06-06
• Status Verified: 2023-11
• Study First Submitted QC: 2023-11-21
• Last Update Submitted: 2023-11-21
• Study First Posted: 2023-11-30
• Last Update Posted: 2023-11-30
• Primary Completion: 2028-02
• Study Completion: 2028-02

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 10000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Develop an artificial intelligence solution to help with diagnosis in VCE	Through study completion, 5 years	Sensitivity in detection of lesions of intermediate to high relevance as identified centrally and consensually by a panel of 3 expert readers (AI vs standard reading)

Secondary Outcome Measures

Name	Time	Method
Evaluation of the diagnostic performance of the A.I. solution in terms of recognition of anatomical landmarks	Through study completion, 5 years	Diagnostic performances in positioning anatomical landmarks (1st gastric, small bowl and colonic images) as identified centrally and consensually by a panel of 3 expert readers (AI vs standard reading)
Evaluation of the diagnostic performance of the A.I. solution in terms of detection	Through study completion, 5 years	Other diagnostic performance (specificity, positive and negative predictive values, accuracy) in detection of lesions of intermediate to high relevance as identified centrally and consensually by a panel of 3 expert readers (AI vs standard reading)
Evaluation of the diagnostic performance of the A.I. solution in terms of quality of preparation of the various segments of the digestive tract	Through study completion, 5 years	Diagnostic performances in quoting small bowel and colonic quality of preparation, as compared toed centrally and consensually quoted by a panel of 3 expert readers
Evaluation of the diagnostic performance of the A.I. solution in terms of characterisation	Through study completion, 5 years	Time needed for detection of lesions of intermediate to high relevance as identified centrally and consensually by a panel of 3 expert readers (AI vs standard reading)

Trial Locations

Locations (1)

Centre d'Endoscopie Digestive Hôpital Saint-Antoine; 🇫🇷 Paris, France