Integrating Plaque and Physiology

Ihdayhid, Abdul Rahman; Jaltotage, Biyanka

doi:10.1016/j.jcmg.2021.05.011

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…15 For cardiac CT angiography, AI can enable automated quantification of luminal stenosis and atherosclerotic plaque, which has been validated against invasive reference standards, 99 and characterization of atherosclerotic plaque into noncalcified, low-attenuation noncalcified, and calcified components. 13 Morphological features of high-risk plaque correspond to future cardiovascular event risk 100 and can be used to guide management, 101 including referral for invasive angiography. 102 These precision medicine systems have the potential to substantially affect patient management in the future.…”

Section: Describing the Steps In The Value Chain For Ai In Cardiac Im...mentioning

confidence: 99%

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

Hanneman,

Playford,

Dey

et al. 2024

Circulation

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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.

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