Key result
AI in multimodality cardiovascular imaging reduces human error and workflow time via automated segmentation.
Why the study?
The exponential growth of cardiovascular imaging studies has created a need to improve clinical workflow efficiency and avoid missed diagnoses.
Artificial intelligence shows significant potential to enhance clinical workflow efficiency and diagnostic accuracy in multimodality cardiovascular imaging, though challenges in data standardization and privacy remain.
May support workflow efficiencies in multimodality imaging; leaves open prospective validation before clinical adoption.
The number of cardiovascular imaging studies is growing exponentially, and so is the need to improve clinical workflow efficiency and avoid missed diagnoses. With the availability and use of large datasets, artificial intelligence (AI) has the potential to improve patient care at every stage of the imaging chain. Current literature indicates that in the short-term, AI has the capacity to reduce human error and save time in the clinical workflow through automated segmentation of cardiac structures. In the future, AI may expand the informational value of diagnostic images based on images alone or a combination of images and clinical variables, thus facilitating disease detection, prognosis, and decision making. This review describes the role of AI, specifically machine learning, in multimodality imaging, including echocardiography, nuclear imaging, computed tomography, and cardiac magnetic resonance, and highlights current uses of AI as well as potential challenges to its widespread implementation.
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Lim et al. (2020) reported a review. Artificial intelligence and machine learning in cardiovascular imaging was evaluated. Artificial intelligence in multimodality cardiovascular imaging demonstrates the capacity to reduce human error and save time in clinical workflow through automated segmentation of cardiac structures.
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