A Comparative Study of AI Methods for Fetal Diagnostic Accuracy in Ultrasound
NCT ID: NCT06268392
Last Updated: 2024-02-22
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
150 participants
OBSERVATIONAL
2024-02-15
2024-08-01
Brief Summary
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Detailed Description
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From the SCAN-AID study ultrasound novices were randomized into one of three groups with different levels of AI support: control group, AI feedback group 1 where the participants are presented with basic black box AI feedback, and AI feedback group 2 where the participants are presented with a more detailed explainable AI feedback. All the participants are tasked to perform an ultrasound fetal weight estimation (EFW) on pregnant women at gestational age 30-37. The outcomes were than compared to the expert sonographers measurements.
In this study an operator independent AI method that predicts the fetal weight is used on the SCAN-AID ultrasound examinations. .
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Expert sonographer
Expert sonographers ultrasound examination.
No interventions assigned to this group
Control Group (CG)
Control group ultrasound examination with no AI support.
No interventions assigned to this group
Feedback group 1 (FG1)
Feedback group 1 ultrasound examination with basic black box AI support.
No interventions assigned to this group
Feedback group 2 (FG2)
Feedback group 2 ultrasound examination with detailed explainable AI support.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* Gestational age: 30-38 weeks
* Maternal age \< 40 years
Exclusion Criteria
* Severe fetal anomaly e.g. fetal heart anomaly, omphalocele etc.
* Severe fetal macrosomia or growth restriction.
18 Years
FEMALE
Yes
Sponsors
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Slagelse Hospital
OTHER
Technical University of Denmark
OTHER
Rigshospitalet, Denmark
OTHER
Copenhagen Academy for Medical Education and Simulation
OTHER
Responsible Party
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Mary Le Ngo
PhD student
Principal Investigators
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Martin Tolsgaard
Role: STUDY_DIRECTOR
CAMES rigshopsitalet
Central Contacts
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Other Identifiers
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CT-2023-11-20-001
Identifier Type: -
Identifier Source: org_study_id
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