SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach

Background. Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity remains to be top health priority in many countries. A non-invasive imaging modality for diagnosis CAD such as single photon emission computed tomography-myocardial perfusion (SPECT-MPI) usually requested by cardiologists it displays radiotracer distribution the heart reflecting myocardial perfusion. The interpretation SPECT-MPI done visually nuclear medicine physician largely dependent on his clinical experience showing significant inter-observer variability.Objective. aim study apply deep learning approach classification abnormalities using convolutional neural networks (CNN).Methods. publicly available anonymized from machine repository (https://www.kaggle.com/selcankaplan/spect-mpi) was used this involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory CNN hyperparameter selection search optimum network model utilized with particular focus various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), number dense nodes (32, 64, 128, 256). base also compared commonly pre-trained CNNs medical images VGG16, InceptionV3, DenseNet121 ResNet50. All simulations experiments were performed Kaggle TensorFlow 2.6.0., Keras 2.6.0, Python language 3.7.10.Results. best performing parameters consisting 0.7 dropout, size 8, 32 generated highest normalized Matthews Correlation Coefficient at 0.909 obtained 93.75% accuracy, 96.00% sensitivity, precision, F1-score. It higher performance architectures.Conclusions. results suggest that approaches through use models can deployed physicians their practice further augment decision skills tests. These dependable valid second opinion aid decision-support tool well serve teaching or materials less-experienced particularly those still training career. highlights utility cardiology.