Development of New Diagnostic Tools in Capsule Endoscopy (NOCE)

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.

Study Overview

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

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 Type: Observational
• Enrollment (Estimated): 10000

Contacts and Locations

• Study Contact: Name: Xavier DRAY, MD PhD; Phone Number: +33 (0) 49 28 21 60; Email: Xavier.dray@aphp.fr
• Study Contact Backup: Name: Romain LEENHARDT, MD; Phone Number: +33 (0) 1 49 28 21 61; Email: romain.leenhardt@aphp.fr

Study Locations

• France
  • Paris, France, 75012
    • Recruiting
    • Centre d'Endoscopie Digestive Hôpital Saint-Antoine
    • Contact: Xavier DRAY, MD PhD; Phone Number: +33 (0) 49 28 21 60; Email: Xavier.dray@aphp.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Any patient in whom a capsule endoscopy examination is performed as part of routine care

Description

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 Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Develop an artificial intelligence solution to help with diagnosis in VCE	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)	Through study completion, 5 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Evaluation of the diagnostic performance of the A.I. solution in terms of detection	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)	Through study completion, 5 years
Evaluation of the diagnostic performance of the A.I. solution in terms of characterisation	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)	Through study completion, 5 years
Evaluation of the diagnostic performance of the A.I. solution in terms of recognition of anatomical landmarks	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)	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	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	Through study completion, 5 years

Collaborators and Investigators

• Sponsor: Assistance Publique - Hôpitaux de Paris

Study record dates

Study Major Dates

• Study Start (Actual): February 10, 2023
• Primary Completion (Estimated): February 1, 2028
• Study Completion (Estimated): February 1, 2028

Study Registration Dates

• First Submitted: June 6, 2023
• First Submitted That Met QC Criteria: November 21, 2023
• First Posted (Actual): November 30, 2023

Study Record Updates

• Last Update Posted (Actual): March 2, 2026
• Last Update Submitted That Met QC Criteria: February 27, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Artificial intelligence; Capsule endoscopy; Small bowel
• Additional Relevant MeSH Terms: Metabolic Diseases; Digestive System Diseases; Gastrointestinal Diseases; Gastroenteritis; Inflammatory Bowel Diseases; Malabsorption Syndromes; Nutritional and Metabolic Diseases; Neoplasms; Crohn Disease; Intestinal Diseases; Celiac Disease
• Other Study ID Numbers: APHP210791

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No