Clinical Validation of an Artificial Intelligence System for De-termining Fetal Lie and Position in 3rd Trimester

Clinical Validation of an Artificial Intelligence System for Determining Fetal Lie and Position in 3rd Trimester

This prospective clinical validation study aims to evaluate the diagnostic performance of an artificial intelligence (AI)-based navigational support system for determining fetal lie and presentation during third-trimester ultrasound examinations. Following routine clinical assessment by an expert clinician, three blind ultrasound sweeps will be obtained from each participant. The AI system will generate predictions of fetal lie and presentation from each sweep, which will be compared with the expert clinician's assessment as the reference standard. The study will also explore clinician perceptions of the system's usability and potential clinical value.

Study Overview

• Status: Recruiting
• Conditions: Pregnancy; Breech Fetal Presentation; Fetal Presentation; Cephalic Fetal Presentation

Detailed Description

Accurate assessment of fetal lie and presentation during the third trimester is essential for obstetric management and delivery planning. Ultrasound is the gold standard for determining fetal orientation; however, the examination is highly operator-dependent and requires considerable experience to correctly identify fetal anatomy and orientation. Artificial intelligence (AI)-based navigational support systems have the potential to assist clinicians by providing real-time guidance during ultrasound examinations and improving the consistency of fetal orientation assessment.

The purpose of this study is to prospectively validate an AI-based navigational support system for determining fetal lie and presentation during routine third-trimester ultrasound examinations. The system analyzes blind ultrasound sweeps acquired along the maternal midline and automatically predicts fetal lie and presentation. AI-generated predictions will be compared with expert clinician assessment, which serves as the reference standard.

Eligible pregnant women attending third-trimester ultrasound examinations at the Fetal Medicine Department, Rigshospitalet, Slagelse - or Hillerød hospital, will be invited to participate. Following written informed consent, an expert clinician will first determine fetal lie and presentation as part of the routine ultrasound examination. The same clinician will subsequently acquire three blind ultrasound sweeps along the maternal midline. The AI navigational support system will analyze each sweep and generate predictions of fetal lie and presentation. All examinations will be completed within 5-10 minutes to minimize changes in fetal orientation between the clinical assessment and AI evaluation.

The primary objective is to evaluate the diagnostic performance of the AI system by comparing AI-generated predictions with expert clinician assessment. Primary outcome measures include overall diagnostic accuracy and agreement,

These findings will provide additional insight into the feasibility of implementing AI-assisted navigational support in routine obstetric ultrasound practice.

• Study Type: Observational
• Enrollment (Estimated): 150

Contacts and Locations

• Study Contact: Name: Mary Le Ngo, MD, MD; Phone Number: +45 20773779; Email: mary.van.anh.le.ngo.01@regionh.dk

Study Locations

• Denmark
  • Slagelse, Denmark, 3450
    • Not yet recruiting
    • Slagelse Hospital, Department of Obstetrics and Gynecology
    • Contact: Kathinka Nyborg, MD; Phone Number: 20773779; Email: kany@regionsjaelland.dk
  • Capital Region
    • Hillerød, Capital Region, Denmark, 3400
      • Not yet recruiting
      • Nordsjællands Hospital, Department of Obstetrics and Gynecology
      • Contact: Gitte Hedermann, Associate Professor; Phone Number: +2520773779; Email: gitte.hedermann.christensen@regionh.dk
  • København Ø
    • Copenhagen, København Ø, Denmark, 2100
      • Recruiting
      • Rigshospitalet - Department of Obstetrics and Gynecology
      • Contact: Martin Tolsgaard, professor,, Medical doctor, Ph.D; Phone Number: +4520773779; Email: martin.groennebaek.tolsgaard@regionh.dk

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Pregnant women with singleton pregnancies attending third-trimester ultrasound examinations at the Fetal Medicine Department, Rigshospitalet, Slagelse Hospital, or Hillerød Hospital.

Description

Inclusion Criteria:

• Pregnant women aged ≥18 years.
• Singleton pregnancy.
• Gestational age ≥28+0 weeks.
• Fetus in longitudinal lie (cephalic or breech presentation) as determined by the routine clinical ultrasound examination.
• Able to understand Danish or English.
• Able and willing to provide written informed consent.

Exclusion Criteria:

• Multiple pregnancy.
• Fetus in transverse lie.
• Inability to provide written informed consent.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Prospective Clinical Validation Cohort; Pregnant women with singleton pregnancies in the third trimester (≥28+0 weeks' gestation) undergoing obstetric ultrasound examination. Participants undergo three blind ultrasound sweeps, and AI-generated predictions of fetal lie and presentation are compared with expert clinician assessment.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic accuracy of the AI navigational support system for determining fetal lie and presentation.	Overall proportion of correct AI predictions of fetal lie and presentation compared with expert clinician assessment (reference standard).	During the study ultrasound examination (approximately 5 minutes).

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Agreement between AI predictions and expert clinician assessment.	Agreement will be evaluated using Cohen's kappa coefficient.	During the study ultrasound examination.

Collaborators and Investigators

• Sponsor: Copenhagen Academy for Medical Education and Simulation
• Collaborators: Rigshospitalet, Denmark

Study record dates

Study Major Dates

• Study Start (Actual): September 11, 2025
• Primary Completion (Estimated): October 1, 2026
• Study Completion (Estimated): October 1, 2026

Study Registration Dates

• First Submitted: August 4, 2026
• First Submitted That Met QC Criteria: August 4, 2026
• First Posted (Actual): August 10, 2026

Study Record Updates

• Last Update Posted (Actual): August 10, 2026
• Last Update Submitted That Met QC Criteria: August 4, 2026
• Last Verified: August 1, 2026

More Information

Terms related to this study

• Keywords: artificial intelligence; Diagnostic accuracy; Navigation system; obstetric ultrasound; clinical validation; fetal lie; fetal presentation; AI-assisted ultrasound; blind ultrasound sweep; third trimester pregnancy; fetal orientation
• Additional Relevant MeSH Terms: Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Obstetric Labor Complications; Pregnancy Complications; Breech Presentation
• Other Study ID Numbers: P-2019-3101

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Individual participant data will not be made publicly available because the dataset contains identifiable medical imaging data and is subject to Danish data protection regulations (GDPR).

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No