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
Descripción general del estudio
Estado
Descripción detallada
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.
Tipo de estudio
Inscripción (Estimado)
Contactos y Ubicaciones
Estudio Contacto
- Nombre: Mary Le Ngo, MD, MD
- Número de teléfono: +45 20773779
- Correo electrónico: mary.van.anh.le.ngo.01@regionh.dk
Ubicaciones de estudio
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Slagelse, Dinamarca, 3450
- Aún no reclutando
- Slagelse Hospital, Department of Obstetrics and Gynecology
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Contacto:
- Kathinka Nyborg, MD
- Número de teléfono: 20773779
- Correo electrónico: kany@regionsjaelland.dk
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Capital Region
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Hillerød, Capital Region, Dinamarca, 3400
- Aún no reclutando
- Nordsjællands Hospital, Department of Obstetrics and Gynecology
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Contacto:
- Gitte Hedermann, Associate Professor
- Número de teléfono: +2520773779
- Correo electrónico: gitte.hedermann.christensen@regionh.dk
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København Ø
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Copenhagen, København Ø, Dinamarca, 2100
- Reclutamiento
- Rigshospitalet - Department of Obstetrics and Gynecology
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Contacto:
- Martin Tolsgaard, professor,, Medical doctor, Ph.D
- Número de teléfono: +4520773779
- Correo electrónico: martin.groennebaek.tolsgaard@regionh.dk
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Método de muestreo
Población de estudio
Descripción
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.
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
Cohortes e Intervenciones
Grupo / Cohorte |
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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.
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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Diagnostic accuracy of the AI navigational support system for determining fetal lie and presentation.
Periodo de tiempo: During the study ultrasound examination (approximately 5 minutes).
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Overall proportion of correct AI predictions of fetal lie and presentation compared with expert clinician assessment (reference standard).
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During the study ultrasound examination (approximately 5 minutes).
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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Agreement between AI predictions and expert clinician assessment.
Periodo de tiempo: During the study ultrasound examination.
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Agreement will be evaluated using Cohen's kappa coefficient.
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During the study ultrasound examination.
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Colaboradores e Investigadores
Colaboradores
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Actual)
Finalización primaria (Estimado)
Finalización del estudio (Estimado)
Fechas de registro del estudio
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- P-2019-3101
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
Descripción del plan IPD
Información sobre medicamentos y dispositivos, documentos del estudio
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