Fetal Health

Fetal health research focuses on improving the accuracy and efficiency of prenatal assessments to enhance pregnancy outcomes. Current efforts leverage machine learning, employing algorithms like support vector machines, random forests, and gradient boosting, to analyze ultrasound images and cardiotocography data for automated biometric measurements, gestational age estimation, and anomaly detection. These AI-driven tools aim to reduce inter-observer variability, improve diagnostic accuracy, and ultimately lead to earlier identification and management of fetal health complications. The resulting advancements promise to significantly improve prenatal care and reduce infant mortality.