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NCT Number: NCT06152289

Development of New Diagnostic Tools in Capsule Endoscopy

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.

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Key information

Conditions

Bowel Disease Celiac Disease Chronic Diarrhea Crohn Disease Digestive System Diseases Gastroenteritis Gastrointestinal Diseases Inflammatory Bowel Diseases Intestinal Diseases Malabsorption Syndromes Metabolic Diseases Neoplasms Nutritional and Metabolic Diseases Tumor

Sex eligibility

All sexes

Study type

Observational

Primary location

Centre d'Endoscopie Digestive Hôpital Saint-Antoine

Paris, 75012, France

Location status: Recruiting

Location contact

Xavier DRAY, MD PhD

CONTACT

[email protected]

+33 (0) 49 28 21 60

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. Develop an artificial intelligence solution to help with diagnosis in VCE

    Time frame: 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 outcomes

  1. Evaluation of the diagnostic performance of the A.I. solution in terms of detection

    Time frame: 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)

  2. Evaluation of the diagnostic performance of the A.I. solution in terms of characterisation

    Time frame: 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)

  3. Evaluation of the diagnostic performance of the A.I. solution in terms of recognition of anatomical landmarks

    Time frame: 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)

  4. 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

    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

Study contacts

Contact information is provided by the study sponsor or research team.

Romain LEENHARDT, MD

CONTACT

[email protected]

+33 (0) 1 49 28 21 61

Xavier DRAY, MD PhD

CONTACT

[email protected]

+33 (0) 49 28 21 60

Sponsors and collaborators

Lead sponsor

Assistance Publique - Hôpitaux de Paris

Other

Registry information

Acronym: NOCE

Important dates

Study start
2023
Primary completion
2028
Study completion
2028
First posted
Nov 30, 2023
Registry last updated
Mar 2, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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