Machine learning for nuclear cardiology: The way forward

Excerpt

Coronary artery disease (CAD) is the single most common cause of death in the developed world—responsible for about 1 in every 5 deaths.1 The major challenge in diagnostic and prognosis of the patients with suspected CAD emanates from the associated comorbidities and the heterogeneity of clinical presentations which modifies the performance of commonly used tests like myocardial perfusion single-photon emission computed tomography (SPECT).2 This variance in diagnostic performance myocardial perfusion SPECT (MPS) can be attributed to a clinician’s difficulty in interpreting the results and extracting information contained in multitude of perfusion and functional parameters. In this regard, machine learning—a subset of artificial intelligence—can leverage the knowledge representation and automated reasoning to detect and extrapolate patterns from the large number of features. To this end, Alonso et al.,3 in this issue of the Journal of Nuclear Cardiology, illustrate the benefits of machine learning techniques for developing a model to enhance the prognostic value from a complex data of the MPS, electrocardiogram, and clinical variables while increasing interpretability. …