Assessment of atherosclerotic plaque burden: comparison of AI-QCT versus SIS, CAC, visual and CAD-RADS stenosis categories

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.

Epicardial Adipose Tissue Attenuation and Fat Attenuation Index: Phantom Study and In Vivo Measurements With Photon-Counting Detector CT

BACKGROUND. Epicardial adipose tissue (EAT) attenuation is a vascular inflammation marker predictive of adverse cardiac events. The fat attenuation index (FAI) assesses fat attenuation for predefined coronary segments. Photon-counting detector (PCD) CT uses routine virtual monoenergetic image (VMI) reconstructions. VMI energy level may affect EAT attenuation and FAI measurements. OBJECTIVE. The purpose of this article was to assess EAT attenuation and FAI measurements at different monoenergetic energy levels in patients undergoing coronary CTA using a first-generation whole-body dual-source PCD CT scanner. METHODS. An anthropomorphic phantom at two sizes with a fat insert was imaged on a first-generation dual-source PCD CT scanner and, as a reference, on a conventional energy-integrating detector (EID) CT scanner at 120 kV. Thirty patients (11 women, 19 men; mean age, 48 ± 10 years; Agatston score < 60) who underwent an ECG-gated unenhanced calcium-scoring scan and contrast-enhanced coronary CTA by PCD CT were retrospectively evaluated. VMIs from 55 to 80 keV at 5-keV increments were reconstructed. EAT attenuation was manually measured on unenhanced and contrast-enhanced images. FAI was calculated using semiautomated software. RESULTS. The attenuation of the phantom fat insert was -69 HU for the reference EID CT; the closest attenuation for PCD CT was observed at 70 keV for the small (-69 HU) and large (-70 HU) phantoms. In patients, EAT attenuation increased for unenhanced acquisition from -111 ± 11 HU at 55 keV to -82 ± 9 HU at 80 keV and for contrast-enhanced acquisition from -104 ± 11 HU at 55 keV to -81 ± 9 HU at 80 keV. The mean attenuation difference between unenhanced and contrast-enhanced scans decreased with increasing energy level (from 7 ± 12 HU to 1 ± 10 HU). The FAI increased from -89 ± 8 HU at 55 keV to -77 ± 12 HU at 80 keV for the right coronary artery, -95 ± 11 HU at 55 keV to -85 ± 11 HU at 80 keV for the left anterior descending artery, and -87 ± 10 HU at 55 keV to -80 ± 12 HU at 80 keV for the circumflex artery. CONCLUSION. EAT attenuation and FAI measurements using PCD CT are impacted by VMI energy level and contrast enhancement. Use of VMI reconstruction at 70 keV provides fat attenuation approximating conventional polychromatic measurements. CLINICAL IMPACT. The findings may help standardize evaluation of pericoronary inflammation by PCD CT as a measure of patients' cardiac risk.

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.