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.
Historical, de-identified neonatal records including ultrasound images, MRI scans, and clinical documentation available for analysis.
Cases with incomplete or missing critical data elements required for AI model analysis.
The population includes retrospective, anonymized neonatal records obtained from clinical datasets, focusing on neonates previously diagnosed or assessed for congenital anomalies and neonatal risks.
Cohort
Retrospective
Not listed
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.
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