Diagnosis of Coronary Artery Disease from Myocardial Perfusion Imaging Using Convolutional Neural Networks

Abstract

Cardiovascular disease is a highly prevalent health problem in both underdeveloped and developing countries worldwide. As such, it remains to be one of the top health priorities in many countries. In coronary artery disease (CAD), the formation of an atherosclerotic plaque is evident in the lumen of blood vessels leading to the derangement in blood flow resulting to diminished delivery of oxygen to the myocardium. Single Photon Emission Computed Tomography – Myocardial Perfusion Imaging (SPECT-MPI) is a usually requested imaging modality to evaluate for CAD. Visual evaluation of the MPI images is performed by a nuclear medicine doctor and is largely dependent on his experience showing significant inter-observer variability. The study aims to assess the performance of convolutional neural networks (CNN) using transfer learning to classify SPECT-MPI for perfusion abnormalities using an anonymized publicly available SPECT-MPI dataset. The pre-processing methods that were applied to the dataset were the following: (a) normalization of images, (b) shuffling of images, (c) train-test split, and (d) geometric augmentation. The pre-processed data was then entered to the popular pre-trained CNNs typically applied to medical images: VGG16, DenseNet121, InceptionV3 and ResNet50. The best performing models were obtained by VGG16 and InceptionV3 with the highest accuracy rate of 84.38%. However, VGG16 had higher recall and F1-scores as compared to InceptionV3 while InceptionV3 had higher precision. Nonetheless, VGG16, InceptionV3 and DenseNet121 obtained similar performance metrics with each other (recall:80-100%, precision: 80.65-100%, F1-scores: 88.89-90.91%) while ResNet50 generated the lowest performance metrics. Overall findings suggest that any of these 3 CNN models (VGG16, InceptionV3, DenseNet121) can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. The models can also be adopted as dependable and trusted secondary assessment which can guide junior doctors seeking consultation for a reliable diagnosis. These models can likewise serve as teaching or learning materials for the less experienced physicians particularly those still in their training career. This highlights the clinical utility of these models in the practice of nuclear cardiology. The results of the research exhibited encouraging outcomes which may possibly be incorporated clinical work. The study has the potential to enrich CAD discernment and monitoring.

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