NCT05538403

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.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
210

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started May 2022

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

May 1, 2022

Completed
4 months until next milestone

First Submitted

Initial submission to the registry

September 9, 2022

Completed
4 days until next milestone

First Posted

Study publicly available on registry

September 13, 2022

Completed
2 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

November 1, 2022

Completed
2 months until next milestone

Study Completion

Last participant's last visit for all outcomes

December 30, 2022

Completed
Last Updated

September 13, 2022

Status Verified

September 1, 2022

Enrollment Period

6 months

First QC Date

September 9, 2022

Last Update Submit

September 9, 2022

Conditions

Fracture

Keywords

AgedRadiographyPaediatric imagingArtificial intelligencechild abuse

Outcome Measures

Primary Outcomes (1)

  • Percentage of fracture detected by AI on radiographs

    Percentage of fracture detected by AI on radiographs

    1 day

Eligibility Criteria

Age0 Years - 2 Years
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17)
Sampling MethodProbability Sample
Study Population

Children aged less than 2 years old in suspected child abuse setting

You may qualify if:

  • 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)

You may not qualify if:

  • Radiograph not interpretable ( poor quality)
  • AI not applicable

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

University hospital

Montpellier, 34295, France

RECRUITING

MeSH Terms

Conditions

Fractures, Bone

Condition Hierarchy (Ancestors)

Wounds and Injuries

Study Officials

  • Ingrid Millet, PUPH

    University Hospital, Montpellier

    STUDY DIRECTOR

Central Study Contacts

Ingrid Millet, PUPH

CONTACT

678887174i-millet@chu-montpellier.fr

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

September 9, 2022

First Posted

September 13, 2022

Study Start

May 1, 2022

Primary Completion

November 1, 2022

Study Completion

December 30, 2022

Last Updated

September 13, 2022

Record last verified: 2022-09

Locations

FR(1)