The field of medicine is being transformed by the integration of artificial intelligence (AI), particularly in cardiovascular care. AI-based approaches, such as machine learning and deep learning algorithms, have revolutionized coronary CT angiography (CCTA), a non-invasive technique used for screening coronary heart disease. AI is enhancing the accuracy of medical diagnoses, improving image quality, and streamlining risk stratification in cardiovascular diseases.
A review published in the journal Medicine Plus explores the role of AI in CCTA and outlines its potential for future advancements in cardiovascular care. One significant advancement facilitated by AI is the optimization of image quality in CCTA. AI-based algorithms, such as generative adversarial networks, have shown promise in rapidly de-noising low-dose cardiac CT images, overcoming challenges associated with low-dose protocols.
Another area where AI is making a significant impact is in the automation of complex assessments. Automated segmentation algorithms have demonstrated good agreement with expert readers and robust repeatability in calculating coronary artery calcium scoring (CACS). This streamlines risk stratification in patient management and improves efficiency.
Accurate assessment of coronary artery stenosis is essential for guiding treatment decisions. AI-based applications, including cloud-based software, have exhibited high diagnostic performance in detecting stenosis. This offers a glimpse into a future where AI plays a central role in diagnostic accuracy and efficiency.
AI-driven algorithms have also reduced the manual adjustment and time required for quantitative plaque analysis in CCTA. This not only improves efficiency but also increases the prognostic value of plaque analysis in patients with poor outcomes.
AI introduces radiomics, an intersection of imaging and AI, as a promising approach to automatically identify high-risk plaque features. Preliminary studies have shown the superiority of CCTA-based radiomics in identifying vulnerable plaques, potentially replacing invasive imaging methods for precision risk stratification.
AI-driven radiomics has also been used to characterize perivascular adipose tissue (PCAT) surrounding coronary arteries, providing insights into cardiovascular dynamics through complex interactions. These innovative approaches hold promise in predicting future cardiovascular events.
AI automates the quantification of epicardial adipose tissue (EAT) volume on CCTA, which has emerged as a potential marker associated with cardiometabolic dysfunction and adverse outcomes. However, further studies are needed to establish standardized ranges and cut-off values for clinical application.
Functional assessment of coronary artery stenosis is crucial for clinical management. AI-based CT-FFR integrates advanced computational fluid dynamics with machine learning and deep learning algorithms. This AI-driven approach provides clinicians with comprehensive insights, bridging the gap between anatomical severity and physiological effects.
While AI has revolutionized cardiovascular care, challenges remain. Overcoming limitations of spatial resolution, standardizing imaging criteria, and developing integrated software are ongoing endeavors. Collaboration between cardiovascular imaging professionals and AI experts is crucial for unlocking the full potential of AI in advancing human health.
In conclusion, AI is reshaping the landscape of cardiovascular diagnostics and care through its role in optimizing image quality, automating assessments, and enhancing precision. The collaboration between healthcare professionals and AI experts is essential in realizing the full potential of AI in medicine, ultimately leading to improved patient outcomes and a healthier future for individuals worldwide.