Fetal Heart

Fetal heart research focuses on developing accurate and non-invasive methods for monitoring fetal cardiac health throughout pregnancy, aiming to improve early detection of potential problems and enhance pregnancy outcomes. Current research emphasizes the use of machine learning, particularly deep learning models like convolutional neural networks and LightGBM classifiers, to analyze fetal heart rate data from various sources, including electrocardiograms and ultrasound images, often incorporating information fusion techniques. These advancements, facilitated by large datasets like OxMat, hold significant promise for improving the accuracy and efficiency of fetal cardiac monitoring, potentially leading to better clinical decision-making and reduced adverse outcomes.