Innovations in Biomedical Engineering: A New Approach to Detecting Fetal and Maternal Cardiac Anomalies

Abstract
In recent years, remarkable advances in biomedical engineering and artificial intelligence (AI) have opened new horizons in the early detection of fetal and maternal cardiac abnormalities. Congenital heart defects (CHDs) remain one of the leading causes of neonatal mortality, and early diagnosis during pregnancy plays a crucial role in improving outcomes.
Deep learning algorithms integrated with advanced ultrasound and Doppler systems can automatically analyze complex motion and blood flow patterns within the fetal heart. For instance, convolutional neural network (CNN)-based models can detect structural anomalies such as ventricular septal defects or valvular stenosis with accuracy exceeding 95%. Additionally, wearable, non-invasive sensors equipped with accelerometers and microphones now enable continuous remote fetal heart rate (FHR) monitoring.
Recent Iranian studies have demonstrated that integrating wearable sensors with AI-based analysis can achieve detection accuracies of up to 98% for fetal distress and significantly reduce unnecessary clinical visits (Jafari Azad, 2023). Beyond diagnostics, these technologies pave the way for innovation and entrepreneurship in digital health, fostering the development of intelligent medical devices and remote monitoring systems.
Thus, modern biomedical engineering serves as a vital bridge between science, innovation, and entrepreneurship — a bridge built to safeguard the lives of both mother and child through data-driven precision medicine.