Machine Learning-Driven Prenatal Brain Anomaly Detection: A Review using Ultrasound Imaging

Artificial intelligence is essential to prenatal medicine because it assist in preventing congenital fetal defects. Despite advancements in ultrasound technology, accurately identifying irregularities remains challenging and time-consuming for medical professionals. Evaluating the algorithms being used to streamline screening for prenatal brain abnormalities is the aim of the current investigation. ML and DL algorithms may be used to optimize fetal diagnostic (ultrasonography) examinations in order to boost the precision of diagnoses for fetal abnormalities, decrease examination time, and alleviate the doctor's burden. This review examines the importance of detecting anomalies in prenatal ultrasound images for the health of the fetus and mother. Additionally, it contrasts the effectiveness and quality of different ML algorithms for detecting fetal brain anomalies. The potential for these revolutionary technologies to improve pre-natal abnormality identification is emphasized, along with the need for further research in this area to improve clinical applications and outcomes.