Machine Learning-based Diagnosis And Risk Classification Of Coronary Artery Disease Using Myocardial Perfusion Imaging Spect: A Radiomics Study

Cardiovascular diseases (CVD) are the most common morbidity and leading cause of mortality worldwide, with coronary artery disease (CAD) being one of the most lethal types. Myocardial Perfusion Imaging (MPI) using single-photon emission computed tomography (SPECT) is a valuable asset for CAD diagnosis since it can non-invasively provide a functional assessment of the myocardium and cardiac arteries. However, the visual interpretation of MPI SPECT has been shown to be observer-dependent, subject to error, and labor-intensive. The exponential increase in the computational power of computers and the introduction of the concepts of data mining and big data have paved the way for the emergence of Artificial Intelligence (AI) methods and Machine Learning (ML) algorithms in medical imaging. ML algorithms can be used to diagnose CAD from MPI SPECT, and have been shown to be more accurate than visual interpretation.