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

NCT06268392 | Not Yet Recruiting DMC

• 1 other identifier: CT-2023-11-20-001
• Study Type: observational
• Target: 150
• Locations: 0 countries
• Sites: N/A

Brief 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.

Conditions

Fetal Growth Retardation; Fetal Macrosomia; Fetal Growth

Keywords

Artificial Intelligence; Fetal weight estimation; Ultrasound

Outcome Measures

Primary Outcomes (2)

• Fetal weight

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

  10 minutes

• 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

Study Arms (4)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Copenhagen Academy for Medical Education and Simulation: lead
• Slagelse Hospital: collaborator
• Technical University of Denmark: collaborator
• Rigshospitalet, Denmark: collaborator

MeSH Terms

Conditions

Fetal Growth Retardation; Fetal Macrosomia

Condition Hierarchy (Ancestors)

Fetal Diseases; Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Growth Disorders; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Diabetes, Gestational; Pregnancy in Diabetics; Diabetes Complications; Diabetes Mellitus; Endocrine System Diseases; Birth Weight; Body Weight; Signs and Symptoms

Study Officials

• Martin Tolsgaard

  CAMES rigshopsitalet

  STUDY DIRECTOR

Central Study Contacts

Mary L Ngo

CONTACT

+4520773779; mary.van.anh.le.ngo@regionh.dk

Martin Tolsgaard

CONTACT

+4538664631; martin.groennebaek.tolsgaard@regionh.dk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PhD student

Study Record Dates

• First Submitted: February 13, 2024
• First Posted: February 20, 2024
• Study Start: February 15, 2024
• Primary Completion: August 1, 2024
• Study Completion: August 1, 2024
• Last Updated: February 22, 2024

Record last verified: 2024-02