Comprehensive Review of Fetal Health Monitoring using DL and ML techniques with Ultrasound and Cardiotocography data

The Artificial Intelligence (AI) has found extensive applications across various research domains, encompassing fields such as health and drug discovery. In the realm of pregnancy, there exist complications or disorders that pose a danger posed by well-being of both the fetus and its pregnant mother. The monitoring of fetal health is a fundamental aspect of prenatal care, playing a vital role in safeguarding the welfare of both the growing fetus and pregnant individuals. This thorough review article explores the crucial domain of fetal health assessment, with particular emphasis on the application of Deep Learning as well Machine Learning methodologies that functions in concert with ultrasound and cardiotocography (CTG) data. Evaluating fetal growth, development, and well-being, therefore establishing a fundamental. This study investigates the multifunctionality of Ultrasonography as a tool for examining fetal anatomy, as well as the informative capabilities of cardiotocography (CTG) in collecting crucial insights via the appraisal of the fetal heart rate patterns throughout the process of labour and delivery. This paper conducts preliminary analysis of the scholarly literature spanning the last five-years (2018-2023) to discern the prevalent methodologies, techniques, algorithms, and frameworks within Machine Learning and Deep Learning applied in multimodal calculating for the health and happiness of pregnant women.