Fetal Cardiac Ultrasound Screening Using Explainable AI

Abstract

Ultrasound images contain more noise than those obtained from imaging modality such as CT (computed tomography) and MRI (magnetic resonance imaging). The development of AI for image diagnosis support using artificial intelligence (AI) is still in its early stages. The authors have focused on fetal cardiac ultrasound screening, which is one of the most difficult ultrasound examinations. The authors have proposed various methods based on the consideration that improving explainability is essential for the widespread use of ultrasound AI in medical practice. The authors propose explainable representations such as barcode-like timeline and graph chart. Furthermore, the authors explain how doctors can improve their screening abilities by using both representations. Finally, the authors introduce their prospects of ultrasound AI research.