AI Driven Early Detection of Cardiovascular Disease Using CT and MRI Scans

Abstract

Diagnosis of cardiovascular disease (CVD) as early as possible would improve methods of managing the disease, reducing mortality and increasing patients' survival. Machine and deep learning methods, fueled with big data from CT and MRI scans, are a paradigm shift in CVD early detection. In more advanced AI systems, various evolutionary approaches, including deep learning and image analysis algorithms, were recently demonstrated to be extraordinarily accurate and swift at detecting both structural and functional pathologies across the cardiovascular system. AI models trained with large datasets of CT and MRI images can detect subtle abnormalities such as plaque formation, myocardial scarring and reduced blood flow, which can be early signs of the higher risk CVD. They demonstrated that AI models achieved sensitivity and specificity rates for the detection of a disease exceeding non AI diagnostic methods translating to improvements in diagnostic accuracy. In addition, it addresses challenges such as data standardization, ethical issues and clinical integration. Such findings on new AI enabled imaging technologies enabling detection of CVD could form the groundwork for much needed personalized interventions that significantly save on expenses and promote the health of individuals and populations through enhanced cardiac health care. Overall, key future directions may include enhancing the generalizability of the algorithms, incorporation of additional multimodal data, and the evolution of dynamics between the models developers and the health care system.