- ICH GCP
- Registro de ensayos clínicos de EE. UU.
- Ensayo clínico NCT04897178
Machine Learning-based Anomaly Recognition System (MARS)
Use of Machine Learning Algorithms for Automated Detection of Fetal Anomalies
Descripción general del estudio
Estado
Condiciones
Intervención / Tratamiento
Descripción detallada
Routine second trimester anomaly scan has become a routine part of antenatal care. Early detection of fetal anomalies permits patient counselling, consideration of termination if detected anomalies are considerable, and arrangement of delivery and immediate neonatal care if indicated. Furthermore, with the expanding role of fetal interventions, early detection of fetal anomalies may expand management options, some of which may lead superior outcomes compared to postnatal interventions.
However, fetal anatomy scan necessitates a particular level of training and expertise, either by sonographers or obstetricians. Unfortunately, availability of experienced personals may be globally limited. Furthermore, first trimester anatomy scan has been evolving rapidly as ultrasound machine continues to develop and clinical research yields more information on first trimester normal standards and abnormal ranges. Accordingly, first trimester scan is anticipated to be a part of routine care in the near future. Although this tool should provide substantial benefits to obstetric patients, this would require more providers with specific training, which is unlikely to be readily available.
Artificial intelligence has been incorporated in the medical field for more than 20 years. With the advancement of deep learning algorithms, deep learning has yielded exceptional accuracy in image recognition. In the last decade, deep learning exhibits high quality performance that may exceed human performance at times. One of the earliest and most prevalent applications of deep learning in medicine are radiology-related.
In the current study, the investigators will create a series of deep learning models that appraise and identify common fetal anomalies in a series of frames including recorded videos or real time ultrasound. Deep learning algorithms will be fed by labelled images of known normal and abnormal findings representing common fetal anomalies for both training and validation. These images will be collected retrospectively through medical records of contributing centers. Their diagnostic performance will be tested on retrospectively collected videos including normal and abnormal findings. In the second stage of the study, These models will be applied to prospectively collected videos of fetal anatomy scan for further validation.
Tipo de estudio
Inscripción (Anticipado)
Contactos y Ubicaciones
Ubicaciones de estudio
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Assiut, Egipto, 71515
- Assiut Faculty of Medicine - Women Health Hospital
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Aswan, Egipto, 81528
- Aswan Faculty of Medicine
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
Acepta Voluntarios Saludables
Géneros elegibles para el estudio
Método de muestreo
Población de estudio
Descripción
Inclusion Criteria:
- Pregnant women between 18 and 45 years
- Available ultrasound image with clear findings
- postnatal confirmation of diagnosis
Exclusion Criteria:
- Absence of research authorization on medical records
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
Cohortes e Intervenciones
Grupo / Cohorte |
Intervención / Tratamiento |
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Fetuses with normal anatomy
Fetuses with normal anatomy scan who demonstrate no structural abnormalities of different systems (CNS, chest and heart, abdomen, skeletal system)
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Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies
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Fetuses with abnormal anatomy
Fetuses with abnormal anatomy scan who demonstrate any structural abnormalities that can be detected with ultrasound
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Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies
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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
Periodo de tiempo: Fetuses between 10 weeks and 32 weeks of gestation
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Diagnostic accuracy of deep learning models in identifying major fetal structural anomalies
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Fetuses between 10 weeks and 32 weeks of gestation
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Colaboradores e Investigadores
Patrocinador
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Anticipado)
Finalización primaria (Anticipado)
Finalización del estudio (Anticipado)
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
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- OBG-AI21-P1
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
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