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AI Performance for the Detection of Bone Fractures in Children

Sponsor
University Hospital, Montpellier (Other)
Overall Status
Recruiting
CT.gov ID
NCT05538403
Collaborator
(none)
210
Enrollment
1
Location
8
Anticipated Duration (Months)
26.3
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

The artificial intelligence (AI) software BoneView (GLEAMER Company, Paris, France) has been designed, tested and validated to detect and locate recent or semi-recent fractures on standard radiographs.

The objective will be to assess the AI performance for the detection of bone fractures in children aged less than 2 years old in suspected child abuse setting.

These patients benefit from a whole body radiography with a double blind reading by a "generalist" radiologist and a radiologist with expertise in child abuse. This readings will be compared with the AI results.

Hypothesis is that AI is effective for child fractures detection and could be of help especially for radiologists who are not experts in child abuse.

Condition or Disease Intervention/Treatment Phase

    Study Design

    Study Type:
    Observational
    Anticipated Enrollment :
    210 participants
    Observational Model:
    Cohort
    Time Perspective:
    Retrospective
    Official Title:
    Assessment of AI Performance for the Detection of Bone Fractures in Children Aged Less Than 2 Years Old in Suspected Child Abuse Setting.
    Actual Study Start Date :
    May 1, 2022
    Anticipated Primary Completion Date :
    Nov 1, 2022
    Anticipated Study Completion Date :
    Dec 30, 2022

    Outcome Measures

    Primary Outcome Measures

    1. Percentage of fracture detected by AI on radiographs [1 day]

      Percentage of fracture detected by AI on radiographs

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    0 Years to 2 Years
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No
    Inclusion criteria:

    • aged less than 2 years old

    • whole body radiography performed for suspected child abuse setting

    • report available with a double blind reading (generalist radiologist and radiologist with expertise in child abuse)

    Exclusion criteria:

    • Radiograph not interpretable ( poor quality)

    • AI not applicable

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 University hospital Montpellier France 34295

    Sponsors and Collaborators

    • University Hospital, Montpellier

    Investigators

    • Study Director: Ingrid Millet, PUPH, University Hospital, Montpellier

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    University Hospital, Montpellier
    ClinicalTrials.gov Identifier:
    NCT05538403
    Other Study ID Numbers:
    • RECHMPL22_0224
    First Posted:
    Sep 13, 2022
    Last Update Posted:
    Sep 13, 2022
    Last Verified:
    Sep 1, 2022
    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 University Hospital, Montpellier
    Aged
    Radiography
    Paediatric imaging
    Artificial intelligence
    child abuse
    Additional relevant MeSH terms:
    Fractures, Bone

    Study Results

    No Results Posted as of Sep 13, 2022