This study aims to develop advanced artificial intelligence (AI) models that predict neonatal risks and complications based on historical multimodal health data, including ultrasound and MRI scans. The objective is to empower clinicians and provide clear, compassionate support for families navigating complex prenatal diagnoses.
The FetalFirst study employs observational, retrospective analysis utilizing DenseNet121 neural networks. It analyzes de-identified retrospective data comprising ultrasound images, MRI scans, and clinical documentation from existing medical records. This research has received ethical approval from Wales Research Ethics Committee (REC ref: 25/WA/0168, IRAS ID: 358793). Outcomes from this study are expected to significantly enhance clinical intervention strategies, offering healthcare professionals robust tools for earlier detection and improved management of congenital anomalies and neonatal risks. Additionally, the insights gained will provide critical support to parents facing high-risk pregnancies, assisting them in making informed decisions.
Study Type
OBSERVATIONAL
Enrollment
50,000
Accuracy of AI Model Predictions for Neonatal Risk
Evaluate the accuracy of DenseNet121-based AI models in predicting neonatal risks and congenital anomalies, measured by sensitivity, specificity, and overall prediction accuracy.
Time frame: 12 Months
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.