Key result
Artificial intelligence and machine learning algorithms demonstrate significant potential to enhance diagnostic accuracy and prognostic risk stratification across multiple cardiovascular imaging modalities.
Why the study?
Conventional statistics are reaching their computational limits in cardiovascular imaging, whereas artificial intelligence and machine learning show promise for improving diagnosis and prognosis.
AI and machine learning have the potential to significantly enhance diagnostic and prognostic capabilities in cardiovascular imaging by automating tasks and unraveling hidden relationships in large datasets.
Requires prospective validation before clinical adoption; leaves open questions on outcome impact and generalizability.
Cardiovascular disease is the leading cause of mortality in Western countries and leads to a spectrum of complications that can complicate patient management. The emergence of artificial intelligence (AI) has garnered significant interest in many industries, and the field of cardiovascular imaging is no exception. Machine learning (ML) especially is showing significant promise in various diagnostic imaging modalities. As conventional statistics are reaching their apex in computational capabilities, ML can explore new possibilities and unravel hidden relationships. This will have a positive impact on diagnosis and prognosis for cardiovascular imaging. In this in-depth review, we highlight the role of AI and ML for various cardiovascular imaging modalities.
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Seetharam et al. (2020) conducted a review in Cardiovascular disease. Artificial Intelligence and Machine Learning was evaluated. Artificial intelligence and machine learning algorithms demonstrate significant potential to enhance diagnostic accuracy and prognostic risk stratification across multiple cardiovascular imaging modalities.
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