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NOCE: Development of New Diagnostic Tools in Capsule Endoscopy

Sponsor
Assistance Publique - Hôpitaux de Paris (Other)
Overall Status
Recruiting
CT.gov ID
NCT06152289
Collaborator
(none)
10,000
Enrollment
1
Location
59.7
Anticipated Duration (Months)
167.5
Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease Intervention/Treatment Phase

    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 Design

    Study Type:
    Observational
    Anticipated Enrollment :
    10000 participants
    Observational Model:
    Cohort
    Time Perspective:
    Prospective
    Official Title:
    Development of New Diagnostic Tools in Capsule Endoscopy
    Actual Study Start Date :
    Feb 10, 2023
    Anticipated Primary Completion Date :
    Feb 1, 2028
    Anticipated Study Completion Date :
    Feb 1, 2028

    Outcome Measures

    Primary Outcome Measures

    1. 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

    1. 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)

    2. 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)

    3. 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)

    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 [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

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    N/A and Older
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No
    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.

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Centre d'Endoscopie Digestive Hôpital Saint-Antoine Paris France 75012

    Sponsors and Collaborators

    • Assistance Publique - Hôpitaux de Paris

    Investigators

    None specified.

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Assistance Publique - Hôpitaux de Paris
    ClinicalTrials.gov Identifier:
    NCT06152289
    Other Study ID Numbers:
    • APHP210791
    First Posted:
    Nov 30, 2023
    Last Update Posted:
    Nov 30, 2023
    Last Verified:
    Nov 1, 2023
    Individual Participant Data (IPD) Sharing Statement:
    Undecided
    Plan to Share IPD:
    Undecided
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Keywords provided by Assistance Publique - Hôpitaux de Paris
    Small bowel
    Capsule endoscopy
    Artificial intelligence
    Additional relevant MeSH terms:
    Crohn Disease
    Celiac Disease
    Diarrhea

    Study Results

    No Results Posted as of Nov 30, 2023