Abstract:

Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.

Objective:

The objective of detecting fetal abnormalities is to identify any structural, functional, or genetic anomalies in the fetus during pregnancy. This early detection is crucial for several reasons: Informed Decision-Making: Parents and healthcare providers can make informed decisions regarding the pregnancy, including the potential need for interventions, specialized care, or planning for the birth of a child with special needs. Early Intervention: Some abnormalities can be treated or managed more effectively if detected early. Early intervention can improve outcomes for both the mother and the baby.

Preparation and Planning: Early detection allows parents to prepare emotionally, financially, and logistically for the arrival of a child who may require special care. It also allows healthcare providers to plan for necessary medical support during and after delivery. Risk Assessment: Identifying abnormalities can provide insights into potential risks for future pregnancies and help in genetic counseling and family planning. Improving Outcomes: Timely detection and appropriate management of fetal abnormalities can improve the overall health outcomes for the fetus and reduce complications during and after birth.