Cardiovascular disease is a leading cause of death in the United States. The identification
of cardiac diseases on conventional three-dimensional (3D) CT can have many clinical
applications. An automated method that can distinguish between healthy and diseased
hearts could improve diagnostic speed and accuracy when the only modality available
is conventional 3D CT. In this work, we proposed and implemented convolutional neural
networks (CNNs) to identify diseased hears on CT images. Six patients with healthy
hearts and six with previous cardiovascular disease events received chest CT. After
the left atrium for each heart was segmented, 2D and 3D patches were created. A subset
of the patches were then used to train separate convolutional neural networks using
leave-one-out cross-validation of patient pairs. The results of the two neural networks
were compared, with 3D patches producing the higher testing accuracy. The full list
of 3D patches from the left atrium was then classified using the optimal 3D CNN model,
and the receiver operating curves (ROCs) were produced. The final average area under
the curve (AUC) from the ROC curves was 0.840 +/- 0.065 and the average accuracy was
78.9% +/- 5.9%. This demonstrates that the CNN-based method is capable of distinguishing
healthy hearts from those with previous cardiovascular disease.
