Artificial Intelligence for Fetal Ultrasound

Diagnostic ultrasound is the most commonly used imaging method in the field of obstetrics and gynecology to estimate various biometrics related to fetal development, fetal well-being, and perinatal prognosis. Until now, ultrasound measurements of fetal health parameters (i.e., amniotic fluid volume, biparietal diameter, head circumference, abdominal circumference, and others) have been made through a cumbersome and time-consuming manual process, and their accuracy depends heavily on the operator’s skill and experience. Therefore, there has been a high demand for an easy-to-use interface for collecting biometrics from fetal ultrasound images to improve clinician workflow efficiency. Traditional methods have fundamental limitations in automating biometric measurements from noisy ultrasound images that are often degraded by signal dropouts, reverberation artifacts, missing boundaries, attenuation, shadows, speckles, and so on. Medical imaging is experiencing a paradigm shift due to the remarkable and rapid advancement of deep learning technology, and ultrasound companies, including Samsung Medison, are making every effort to develop a new AI-based system for automated fetal ultrasound diagnosis. The reason for these efforts of ultrasound companies is that AI technology is expected to become a turning point in diagnostic ultrasound. This chapter focuses on fetal ultrasound, explains deep learning-based medical imaging technology, and hopes to help readers discover new possibilities and to provide future directions.