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

4. august 2026 oppdatert av: Mary Le Ngo, Copenhagen Academy for Medical Education and Simulation

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.

Studieoversikt

Status

Rekruttering

Forhold

Detaljert beskrivelse

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.

Studietype

Observasjonsmessig

Registrering (Antatt)

150

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiekontakt

Studiesteder

  • Danmark
      • Slagelse, Danmark, 3450
        • Har ikke rekruttert ennå
        • Slagelse Hospital, Department of Obstetrics and Gynecology
        • Ta kontakt med:
    • Capital Region
      • Hillerød, Capital Region, Danmark, 3400
        • Har ikke rekruttert ennå
        • Nordsjællands Hospital, Department of Obstetrics and Gynecology
        • Ta kontakt med:
    • København Ø
      • Copenhagen, København Ø, Danmark, 2100
        • Rekruttering
        • Rigshospitalet - Department of Obstetrics and Gynecology
        • Ta kontakt med:

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

  • Voksen
  • Eldre voksen

Tar imot friske frivillige

Nei

Prøvetakingsmetode

Ikke-sannsynlighetsprøve

Studiepopulasjon

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

Beskrivelse

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.

Studieplan

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

Kohorter og intervensjoner

Gruppe / Kohort
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.

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Diagnostic accuracy of the AI navigational support system for determining fetal lie and presentation.
Tidsramme: During the study ultrasound examination (approximately 5 minutes).
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).

Sekundære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Agreement between AI predictions and expert clinician assessment.
Tidsramme: During the study ultrasound examination.
Agreement will be evaluated using Cohen's kappa coefficient.
During the study ultrasound examination.

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

Copenhagen Academy for Medical Education and Simulation

Samarbeidspartnere

Rigshospitalet, Denmark

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Faktiske)

11. september 2025

Primær fullføring (Antatt)

1. oktober 2026

Studiet fullført (Antatt)

1. oktober 2026

Datoer for studieregistrering

Først innsendt

4. august 2026

Først innsendt som oppfylte QC-kriteriene

4. august 2026

Først lagt ut (Faktiske)

10. august 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

10. august 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

4. august 2026

Sist bekreftet

1. august 2026

Mer informasjon

Begreper knyttet til denne studien

Nøkkelord

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • P-2019-3101

Plan for individuelle deltakerdata (IPD)

Planlegger du å dele individuelle deltakerdata (IPD)?

NEI

IPD-planbeskrivelse

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).

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

Denne informasjonen ble hentet direkte fra nettstedet clinicaltrials.gov uten noen endringer. Hvis du har noen forespørsler om å endre, fjerne eller oppdatere studiedetaljene dine, vennligst kontakt register@clinicaltrials.gov. Så snart en endring er implementert på clinicaltrials.gov, vil denne også bli oppdatert automatisk på nettstedet vårt. .

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