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Machine Learning Integration of SPECT Myocardial Perfusion Imaging and Clinical Data in MACE

出版	2017
URL	http://books.google.com.hk/books?id=PAS2zQEACAAJ&hl=&source=gbs_api

註釋Introduction: Over the last decade, implementation of machine learning (ML) algorithms in cardiology, particularly in the field of nuclear cardiology, has revolutionized individualized diagnosis and prognostic estimations of myocardial ischemia-related major adverse cardiovascular events (MACE). The use of non-invasive, high-quality imaging via single-photon emission computed tomography (SPECT), together with clinical data, improves accuracy of diagnosis and prognosis of MACE compared to traditional methods. The aim of this review is to analyze the evidence for diagnostic and prognostic estimations of the combined use of SPECT myocardial perfusion imaging (MPI) and clinical data using ML. Methods: A literature search of databases (e.g., PubMed and SpringerLink) was refined to identify reports of combined SPECT MPI and clinical data using ML focusing on diagnostic and prognostic values in MACE outcomes (e.g., non-fatal myocardial infarction, unstable angina, and coronary revascularization). Results: Three cohort studies were included for review. A boosting ML ensemble of MPI total perfusion deficits (TPD) and clinical variables (e.g., age, sex, and post-electrocardiogram CAD probability) for detection of CAD were compared to standard imaging quantification and to visual analysis by two experienced readers. The diagnostic accuracy of ML (87.3%) was similar to expert 1 (86.0%), but superior to combined supine and prone TPD (82.8%) and expert 2 (82.1%, p