Diagnostic AI and Cardiac Diseases

Dear Colleagues, 

Worldwide, cardiac diseases are the leading cause of mortality. These health conditions affect the heart, as well as the vascular system that supports the heart, brain, and other organs. Unhealthy eating, inactivity, cigarette and alcohol usage are the most significant behavioral risk factors for heart disease. Individuals with these conditions may experience hypertension, hyperglycemia, and elevated cholesterol as a result of behavioral risk factors. These factors can be assessed in primary care settings and point to an elevated risk of consequences, such as heart attack, stroke, and heart failure. Chest pain, angina, respiratory problems, cyanosis, exhaustion, dizziness, and swelling from fluid retention and edema are the signs of heart abnormalities.

Patients with heart diseases have a high death rate mostly due to late diagnosis or misdiagnosis, which results in late treatment. Normally, blood tests, electrocardiogram tests, coronary angiograms, chest X-ray tests, etc. are carried out following diagnosis. Although these tests are used, there is still a gap in early detection, especially when the cardiac condition is in its infant stages. Thus, more effective systems are needed to support physicians in diagnosis. Recently, artificial intelligence (AI) models have been incorporated into clinical cases to offer solutions to various health issues.

Various models have been built for the diagnosis, risk assessment, prognosis, and treatment planning of patients with heart diseases through subsets of AI. The main objectives of AI in cardiac medicine are to enhance patient care, increase effectiveness, and enhance clinical outcomes. Cardiology is well-positioned to benefit from AI, due to the expansion of possible sources of new patient data and improvements in research and treatments. Lately, advances in AI in cardiology have given rise to a variety of uses, including accurate disease classification, the integration of many imaging modalities, ongoing telemonitoring and diagnosis, treatment management, and AI-aided diagnosis.

The goal of this Special Issue is to provide an insight into the state-of-the-art in this field, evaluate various aspects, and identify potential future possibilities for artificial intelligence applications in cardiac disease diagnosis.

Dr. Dilber Uzun Ozsahin
Prof. Dr. Tahir Yagdi
Dr. Ilker Ozsahin
Guest Editors

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