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.

Background: The future risk of myocardial infarction is commonly assessed using cardiovascular risk scores, coronary artery calcium score, or coronary artery stenosis severity. We assessed whether noncalcified low-attenuation plaque burden on coronary CT angiography (CCTA) might be a better predictor of the future risk of myocardial infarction. Methods: In a post hoc analysis of a multicenter randomized controlled trial of CCTA in patients with stable chest pain, we investigated the association between the future risk of fatal or nonfatal myocardial infarction and low-attenuation plaque burden (% plaque to vessel volume), cardiovascular risk score, coronary artery calcium score or obstructive coronary artery stenoses. Results: In 1769 patients (56% male; 58±10 years) followed up for a median 4.7 (interquartile interval, 4.0–5.7) years, low-attenuation plaque burden correlated weakly with cardiovascular risk score ( r =0.34; P <0.001), strongly with coronary artery calcium score ( r =0.62; P <0.001), and very strongly with the severity of luminal coronary stenosis (area stenosis, r =0.83; P <0.001). Low-attenuation plaque burden (7.5% [4.8–9.2] versus 4.1% [0–6.8]; P <0.001), coronary artery calcium score (336 [62–1064] versus 19 [0–217] Agatston units; P <0.001), and the presence of obstructive coronary artery disease (54% versus 25%; P <0.001) were all higher in the 41 patients who had fatal or nonfatal myocardial infarction. Low-attenuation plaque burden was the strongest predictor of myocardial infarction (adjusted hazard ratio, 1.60 (95% CI, 1.10–2.34) per doubling; P =0.014), irrespective of cardiovascular risk score, coronary artery calcium score, or coronary artery area stenosis. Patients with low-attenuation plaque burden greater than 4% were nearly 5 times more likely to have subsequent myocardial infarction (hazard ratio, 4.65; 95% CI, 2.06–10.5; P <0.001). Conclusions: In patients presenting with stable chest pain, low-attenuation plaque burden is the strongest predictor of fatal or nonfatal myocardial infarction. These findings challenge the current perception of the supremacy of current classical risk predictors for myocardial infarction, including stenosis severity. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01149590.

Deep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods.

IMPORTANCE Pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation measured from coronary CT angiography (CTA) may be a promising metric in identifying high-risk plaques. OBJECTIVE To determine whether high-risk plaque characteristics from coronary CTA are associated with PCAT CT attenuation in patients with a first acute coronary syndrome (ACS) and matched controls with stable coronary artery disease (CAD). DESIGN, SETTING, AND PARTICIPANTSThis retrospective, single-center case-control study (data were acquired at the University of Erlangen from 2009-2010) analyzed the CTA data sets of 19 patients who presented with ACS and 16 controls with stable CAD who were matched based on sex, age, and risk factors. Study observers were blinded to patients' clinical data. Semiautomated software was used to quantify and characterize plaques. The CT attenuation (Hounsfield unit [HU]) of PCAT was automatically measured around all lesions. MAIN OUTCOMES AND MEASURES To investigate the association between high-risk plaque characteristics from CTA and PCAT CT attenuation as a novel surrogate measure of coronary inflammation. RESULTS A total of 35 patients (mean [SD] age, 59.5 [11.3] years; 30 men [86%] and 5 women [14%]) were included in the analysis. Low-and intermediate-attenuation noncalcified plaque (NCP) burden were increased in culprit lesions (n = 19) compared with both nonculprit lesions (n = 55) in patients with ACS (12.6% vs 3.6%; P < .001; 38.4% vs 19.4%; P < .001) and the control group's highest-grade stenosis lesions (n = 16) (12.6% vs 5.6%; P = .002; 38.4% vs 22.1%; P < .001). Pericoronary adipose tissue attenuation was increased around culprit lesions (n = 19) compared with nonculprit lesions (n = 55) in patients with ACS (−69.1 HU vs −74.8 HU; P = .01) and highest-grade stenosis lesions in control patients (n = 16) (−69.1 HU vs −76.4 HU; P = .01). Pericoronary adipose tissue CT attenuation of all lesions in patients with ACS (n = 74) correlated more strongly with intermediate-attenuation (r = 0.393; P = .001) over low-attenuation (r = 0.221; P = .06) and high-attenuation NCP burden (r = −0.103; P = .38). In a multivariable analysis, low-and intermediate-attenuation NCP burden and PCAT CT attenuation were independently associated with the presence of culprit lesions (P < .05). CONCLUSIONS AND RELEVANCE Pericoronary CT attenuation was increased around culprit lesions compared with nonculprit lesions of patients with ACS and the lesions of matched controls. Combined quantitative high-risk plaque features and PCAT CT attenuation may allow for a more reliable identification of vulnerable plaques.

EAT volume was higher and density lower in subjects with coronary calcium compared to subjects with CCS = 0, with similar EAT volume in CCS<100 and CCS≥100. Lower EAT density and increased EAT volume were associated with coronary calcification, serum levels of plaque inflammatory markers and MACE, suggesting that dysfunctional EAT may be linked to early plaque formation and inflammation.

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Aims Increased attenuation of pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) from coronary computed tomography angiography (CTA) has been shown to be associated with coronary inflammation and improved prediction of cardiac death over plaque features. Our aim was to investigate whether PCAT CT attenuation is related to progression of coronary plaque burden. Methods and results We analysed CTA studies of 111 stable patients (age 59.2 ± 9.8 years, 77% male) who underwent sequential CTA (3.4 ± 1.6 years between scans) with identical acquisition protocols. Total plaque (TP), calcified plaque (CP), non-calcified plaque (NCP), and low-density non-calcified plaque (LD-NCP) volumes and corresponding burden (plaque volume × 100%/vessel volume) were quantified using semi-automated software. PCAT CT attenuation (HU) was measured around the proximal RCA, the most standardized method for PCAT analysis. Patients with an increase in NCP burden (n = 51) showed an increase in PCAT attenuation, whereas patients with a decrease in NCP burden (n = 60) showed a decrease {4.4 [95% confidence interval (CI) 2.6–6.2] vs. −2.78 (95% CI −4.6 to −1.0) HU, P < 0.0001}. Changes in PCAT attenuation correlated with changes in the burden of NCP (r = 0.55, P < 0.001) and LD-NCP (r = 0.24, P = 0.01); but not CP burden (P = 0.3). Increased baseline PCAT attenuation ≥−75 HU was independently associated with increase in NCP (odds ratio 3.07, 95% CI 1.4–7.0; P < 0.008) and TP burden on follow-up CTA. Conclusion PCAT attenuation measured from routine CTA is related to the progression of NCP and TP burden. This imaging biomarker may help to identify patients at increased risk of high-risk plaque progression and allow monitoring of beneficial changes from medical therapy.

The number of iterations and extent of post-filtering has marked effects on coronary F-NaF PET quantification. Cardiac motion-correction improves discrimination between culprit and reference lesions.