Artificial Intelligence in Cardiovascular Imaging

Ali Fuat TEKİNa , Yiğit Can KARTALa
aİstanbul Başakşehir Çam ve Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye

Tekin AF, Kartal YC. Artificial intelligence in cardiovascular imaging. In: Bayraktaroğlu S, ed. Advances in Cardiovascular System Imaging. 1st ed. Ankara: Türkiye Klinikleri; 2024. p.74-8.

ABSTRACT
Artificial intelligence (AI) in cardiovascular imaging plays a significant role in the diagnosis and management of diseases. Techniques like deep learning and machine learning enhance image analysis and interpretation, improving diagnostic accuracy and efficiency. Models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) are used for image segmentation, disease detection, and treatment planning. AI is also employed to enhance image quality and accelerate diagnostic processes in cardiovascular imaging. However, challenges such as data quality, clinical integration, and ethical issues persist. Overcoming these challenges is crucial for the successful transition of AI technologies into clinical practice, ultimately improving patient care.

Keywords: Artificial intelligence; deep learning; image segmentation; diagnostic accuracy; cardiac imaging

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