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Not yet recruitingNCT06268392Updated Feb 22, 2024

A Comparative Study of AI Methods for Fetal Diagnostic Accuracy in Ultrasound

An observational study in Fetal Growth Retardation, Fetal Macrosomia and Fetal Growth, sponsored by Copenhagen Academy for Medical Education and Simulation. Not yet recruiting. Open to female participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-02-22.

Sponsored by Copenhagen Academy for Medical Education and Simulation · Observational

Study type
Observational
Model
Cohort
Time perspective
Retrospective
Enrollment
150
Ages
18 Years and older
Sex
Female
01

Study summary

This study serves as a supplemental investigation to the randomized controlled SCAN-AID study (NCT0632187). This study will evaluate and compare the fetal growth estimation outcomes of AI-supported groups, expert sonographers, and control groups using a secondary AI predictive model.

Read the detailed description

The goal of this study is to compare the effects of two distinct AI methods on fetal ultrasound diagnostic accuracy. It serves as a supplementary investigation to the SCAN-AID study (NCT NCT06232187). This study aims will compare the diagnostic accuracy of two types of AI methods.

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. .

02

Conditions studied

  • Fetal Growth Retardation
  • Fetal Macrosomia
  • Fetal Growth

Keywords

  • Artificial Intelligence
  • Fetal weight estimation
  • Ultrasound
03

Who can participate

Ages eligible
18 Years and older
Sexes eligible
Female
Accepts healthy volunteers
Yes
Sampling method
Non-probability sample

Study population

Medical students are recruited from the Medical universities in Denmark (mainly Copenhagen University) Pregnant women are chosen from the fetal medical departement at Rigshospitalet, Copenhagen.

Pregnant women:

Inclusion criteria

Inclusion Criteria:

  • Singelton pregnant.
  • Gestational age: 30-38 weeks
  • Maternal age \< 40 years

Exclusion criteria

Exclusion Criteria:

  • Oligo hydramnion
  • Severe fetal anomaly e.g. fetal heart anomaly, omphalocele etc.
  • Severe fetal macrosomia or growth restriction.
04

Study design

Observational model
Cohort
Time perspective
Retrospective
Enrollment
150 participants (estimated)
Patient registry
No

Groups and cohorts

  • Expert sonographer

    Expert sonographers ultrasound examination.

  • Control Group (CG)

    Control group ultrasound examination with no AI support.

  • Feedback group 1 (FG1)

    Feedback group 1 ultrasound examination with basic black box AI support.

  • Feedback group 2 (FG2)

    Feedback group 2 ultrasound examination with detailed explainable AI support.

05

What researchers measure

Primary outcomes

  1. Fetal weight

    Estimation of fetal weight, generated from AI analysis of fetal ultrasound images.

    Time frame: 10 minutes

  2. Ultrasound fetal weight estimation

    Estimation of fetal weight, calculated from hadlock formula with information from the three standard planes of the head, abdomen and femur.

    Time frame: 15 minutes

06

Study locations

No study locations are listed for this record.

07

References and documents

Individual participant data

Plan to share: Undecided

No publications or documents are linked to this record.

08

Registry details

Key details

Study ID
NCT06268392
Lead sponsor
Copenhagen Academy for Medical Education and Simulation
Collaborators
Slagelse Hospital, Technical University of Denmark, Rigshospitalet, Denmark
Responsible party
Mary Le Ngo (PhD student, Copenhagen Academy for Medical Education and Simulation) — Principal investigator
First posted
Feb 20, 2024
Start date
Feb 15, 2024 (estimated)
Primary completion
Aug 1, 2024 (estimated)
Completion
Aug 1, 2024 (estimated)
Last update
Feb 22, 2024

Study contacts

Mary L Ngo
Contact
mary.van.anh.le.ngo@regionh.dk
+4520773779
Martin Tolsgaard
Contact
martin.groennebaek.tolsgaard@regionh.dk
+4538664631
Martin Tolsgaard
study director · CAMES rigshopsitalet

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is not yet recruiting, as verified in Feb 2024. You cannot join it, but the record below documents what was studied.

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