![]() Methods: 64 patients were with suspected CAD were enrolled in this study, the mean age was 51 ± 24 years, 36 (56%) male and 28 (44%) female. The goal of the study is to investigate the diagnostic value of CTCA after non-diagnostic, equivocal, and or borderline abnormal MP-SPECT. Computed Tomography coronary angiography (CTCA) has high diagnostic accuracy and excellent negative predicted value. However, Infrequently, MP-SPECT is non-diagnostic or equivocal. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.Background: Myocardial perfusion single- photon emission computed tomography (MP-SPECT) plays a key role in the management of patients with known or suspected coronary artery disease (CAD). The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. This use will continue to increase with new cardiotoxic cancer treatments. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. Cardiovascular disease is a leading cause of death in cancer survivors. ![]()
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