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
Studieöversikt
Status
Detaljerad beskrivning
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.
Studietyp
Inskrivning (Beräknad)
Kontakter och platser
Studiekontakt
- Namn: Mary Le Ngo, MD, MD
- Telefonnummer: +45 20773779
- E-post: mary.van.anh.le.ngo.01@regionh.dk
Studieorter
-
-
-
Slagelse, Danmark, 3450
- Har inte rekryterat ännu
- Slagelse Hospital, Department of Obstetrics and Gynecology
-
Kontakt:
- Kathinka Nyborg, MD
- Telefonnummer: 20773779
- E-post: kany@regionsjaelland.dk
-
-
Capital Region
-
Hillerød, Capital Region, Danmark, 3400
- Har inte rekryterat ännu
- Nordsjællands Hospital, Department of Obstetrics and Gynecology
-
Kontakt:
- Gitte Hedermann, Associate Professor
- Telefonnummer: +2520773779
- E-post: gitte.hedermann.christensen@regionh.dk
-
-
København Ø
-
Copenhagen, København Ø, Danmark, 2100
- Rekrytering
- Rigshospitalet - Department of Obstetrics and Gynecology
-
Kontakt:
- Martin Tolsgaard, professor,, Medical doctor, Ph.D
- Telefonnummer: +4520773779
- E-post: martin.groennebaek.tolsgaard@regionh.dk
-
-
Deltagandekriterier
Urvalskriterier
Åldrar som är berättigade till studier
- Vuxen
- Äldre vuxen
Tar emot friska volontärer
Testmetod
Studera befolkning
Beskrivning
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
Hur är studien utformad?
Designdetaljer
Kohorter och interventioner
Grupp / 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.
|
Vad mäter studien?
Primära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
|
Diagnostic accuracy of the AI navigational support system for determining fetal lie and presentation.
Tidsram: 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).
|
Sekundära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
|
Agreement between AI predictions and expert clinician assessment.
Tidsram: During the study ultrasound examination.
|
Agreement will be evaluated using Cohen's kappa coefficient.
|
During the study ultrasound examination.
|
Samarbetspartners och utredare
Samarbetspartners
Studieavstämningsdatum
Studera stora datum
Studiestart (Faktisk)
Primärt slutförande (Beräknad)
Avslutad studie (Beräknad)
Studieregistreringsdatum
Först inskickad
Först inskickad som uppfyllde QC-kriterierna
Första postat (Faktisk)
Uppdateringar av studier
Senaste uppdatering publicerad (Faktisk)
Senaste inskickade uppdateringen som uppfyllde QC-kriterierna
Senast verifierad
Mer information
Termer relaterade till denna studie
Nyckelord
Ytterligare relevanta MeSH-villkor
Andra studie-ID-nummer
- P-2019-3101
Plan för individuella deltagardata (IPD)
Planerar du att dela individuella deltagardata (IPD)?
IPD-planbeskrivning
Läkemedels- och apparatinformation, studiedokument
Studerar en amerikansk FDA-reglerad läkemedelsprodukt
Studerar en amerikansk FDA-reglerad produktprodukt
Denna information hämtades direkt från webbplatsen clinicaltrials.gov utan några ändringar. Om du har några önskemål om att ändra, ta bort eller uppdatera dina studieuppgifter, vänligen kontakta register@clinicaltrials.gov. Så snart en ändring har implementerats på clinicaltrials.gov, kommer denna att uppdateras automatiskt även på vår webbplats .