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

Fetal Growth Retardation Clinical Trial

Official title:

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06268392
• Other study ID #: CT-2023-11-20-001
• Status: Not yet recruiting
• Start date: February 15, 2024
• Est. completion date: August 1, 2024

Study information

• Verified date: February 2024
• Source: Copenhagen Academy for Medical Education and Simulation
• Contact: Mary L Ngo
• Phone: +4520773779
• Email: mary.van.anh.le.ngo[@]regionh.dk
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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. .

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 150
• Est. completion date: August 1, 2024
• Est. primary completion date: August 1, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 18 Years and older

Eligibility

Pregnant women:

Inclusion Criteria:

• Singelton pregnant.

• Gestational age: 30-38 weeks

• Maternal age < 40 years

Exclusion Criteria:

• Oligo hydramnion

• Severe fetal anomaly e.g. fetal heart anomaly, omphalocele etc.

• Severe fetal macrosomia or growth restriction.

Related Conditions & MeSH terms

• Fetal Growth
• Fetal Growth Retardation
• Fetal Macrosomia

Locations

Country	Name	City	State
n/a

Sponsors (4)

Lead Sponsor	Collaborator
Copenhagen Academy for Medical Education and Simulation	Rigshospitalet, Denmark, Slagelse Hospital, Technical University of Denmark

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Fetal weight	Estimation of fetal weight, generated from AI analysis of fetal ultrasound images.	10 minutes
Primary	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.	15 minutes