A deep learning-based intelligent analysis platform for fetal ultrasound four-chamber views

Abstract

The four-chamber view is the primary ultrasound images that clinicians diagnose whether a fetus has congenital heart disease (CHD) in the process of prenatal diagnosis and screening, which can provide clinicians with a clear view of the developmental morphology of the fetal four chambers (i.e., left atrium, left ventricle, right atrium, and right ventricle). The early diagnosis and screening for CHD depend on the clinicians' experience to a large extent. Deep learning technology has achieved great success in medical image analysis. Hence, applying deep learning technology in the four-chamber view analysis can help improve the diagnostic accuracy of CHD and make it more objective. Hence, we design a deep learning-based intelligent analysis platform (DLIAP) for fetal ultrasound four-chamber views, which includes an image input module, an image analysis module, a visualization output module, and an information query module. The DLIAP can assist the clinicians in objectively analyzing the fetal ultrasound four-chamber views and further improve the diagnostic accuracy of CHD.