Detection of Congenital Heart Disease in MR images using Machine Learning

We present a new method for detection of hypoplastic left heart syndrome (HLHS) based on the spatial arrangement of 7 distinctive anatomical landmarks in CMR images. The method was applied to the axial SSFP CMR scans of 46 patients with HLHS and 33 healthy controls. A tailor-made U-net-like deep convolutional network (CNN) with a shared 3D-convolutional encoder backbone and 7 segmentation heads was used for prediction of landmarks. Classification based exclusively on the coordinates of the detected landmarks had an accuracy of 98.7%. In future studies, the method may be applied to HLHS subgroups or other cardiac diseases.