Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Abstract

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

Noninvasive Pressure-Volume Loops

“…However, artificial intelligence applications with in-line automated analysis of PV loop parameters could conceivably be integrated into existing cardiac MRI post-processing software workflows. 6 Future studies should evaluate the incremental value of PV loop parameters to ejection fraction and expand beyond patients with HFrEF to include heart failure with preserved ejection fraction, valvular heart disease, and pulmonary hypertension. Leveraging information from routine noninvasive imaging has the potential to obviate the need for invasive assessments, reducing cost, risk, and environmental impact.…”