Artificial Intelligence in Cardiovascular Imaging

By Damini Dey, Piotr J. Slomka, Paul Leeson, Dorin Comaniciu, S. Shrestha, Partho P. Sengupta, T. Marwick. Journal of the American College of Cardiology, 2019

546 citations recorded by the source corpus.

Data science is likely to lead to major changes in cardiovascular imaging. Problems with timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The application of artificial intelligence (AI) is dependent on robust data; the application of appropriate computational approaches and tools; and validation of its clinical application to image segmentation, automated measurements, and eventually, automated diagnosis. AI may reduce cost and improve value at the stages of image acquisition, interpretation, and decision-making. Moreover, the precision now possible with cardiovascular imaging, combined with “big data” from the electronic health record and pathology, is likely to better characterize disease and personalize therapy. This review summarizes recent promising applications of AI in cardiology and cardiac imaging, which potentially add value to patient care.

Subfields: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/33a995a3376568f1036b14522db5b783c34ba463

Citations in the corpus

• The ‘Digital Twin’ to enable the vision of precision cardiology (2020, unknown): “We review the state of the art of the interplay between such models that supports this vision, considering that there are already excellent independent review papers in the fields of statistical 14–16 and mechanistic 17,18 models for cardiovascular medicine.”