In the present article, an overview of advanced analysis of coronary atherosclerosis by coronary computed tomography angiography (CCTA) is provided, focusing on the potential application of this technique in a primary prevention setting. Coronary artery calcium score (CACS) has a well-demonstrated prognostic value even in a primary prevention setting; however, fibro-fatty, high-risk coronary plaque may be missed by this tool. On the contrary, even if not recommended for primary prevention in the general population, CCTA may enable early high-risk atherosclerosis detection, and specific subgroups of patients may benefit from its application. However, further studies are needed to determine the possible use of CCTA in a primary prevention setting.

Background Cardiac computed tomography (CT) emerged as an accurate tool for non-invasive evaluation of coronary artery disease (CAD), being able to identify high risk non-calcified atherosclerosis. Identification of high risk CAD in its asymptomatic stage could be an interest target for medical therapy. Nowadays no validated tools are available to predict the presence of high risk atherosclerosis, probably due to the multifactorial pathogenesis of atherosclerosis. Facial features may express both genetic and environmental factors that could be associated to high risk atherosclerosis. Aim of the present study was to verify whether deep learning models applied to facial features may accurately predict the presence of high risk coronary atherosclerosis evaluated at cardiac CT Methods We enrolled a consecutive cohort of patients who underwent clinical indicated cardiac CT for suspected, CAD. Before CT, 10 facial photos were taken from every patients from random fronts views. All cardiac CT were analysed for the presence of non-calcified plaque volume (defined as <150 HU at CT); the non-calcified plaque volume was quantified on a per-patient basis in mm3 and a cut off of >23 mm3 was used to define a patients with an elevated volume non-calcified plaque We built a deep learning model, exploiting the transfer learning technique; briefly, we implemented an “xception” architecture, joining a pre-trained convolutional part with a specific combination of dense layers, in which an output layer follows a hidden layer with 512 neurons and a dropout layer with a dropout rate=0.2. The batch size, the number of epochs and the learning rate were 16, 20, and 0.0001, respectively. A training set composed of 198 face images was fed into the model, while 20 face images served as test set for the prediction of the presence of elevated volume of non-calcified plaque from patients facial features. Results We present early results from the first 20 patients enrolled (12 male and 8 female, with mean age of 73±13 years old). In 9 patients cardiac CT resulted completely normal, while in 11 subjects the presence of coronary atherosclerosis was demonstrated. Among them, 9 patients presented non-calcified coronary atherosclerosis, while 6 had an elevated volume of non-calcified plaque. On the test set, we obtained an accuracy, sensitivity, specificity, positive predictive value, negative predictive values and and AUC equal to 0.90, 1, 0.8, 0.83, 1, and 0.99, respectively for the prediction of the presence of an elevated volume of non-calcified plaque from facial features among all 20 patients enrolled. Conclusions Prediction of the presence of high risk atherosclerosis from deep learning models applied to facial features appeared to be feasible and promising. Our results may provide a useful tool for appropriate identification of patients that may merit to underwent cardiac CT, even if asymptomatic, for early identification of high risk atherosclerosis Funding Acknowledgement Type of funding sources: None.

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Introduction Negative T waves at ECG represent a common diagnostic dilemma in athletes. These subjects, often asymptomatic, undergo ECG screening every year before practicing competitive sports. The clinical meaning of these ECG abnormalities is often unclear and a comprehensive diagnostic evaluation is needed. Echocardiography is the first step test in all these cases, but the advent of cardiac MRI in the clinical field empowers the diagnostic capability for the identification of cardiovascular disease at a very early stage, even when transthoracic echocardiography is normal. The aim of the present study is to define the prevalence of positive cardiac MRI among athletes with negative T waves at ECG and normal echocardiography and to define the clinical predictors of pathological cardiac MRI or cardiac CT Material and Methods A consecutive cohort of athletes with negative T waves at ECG and normal findings at transthoracic echocardiography were enrolled. All athletes underwent 24h ECG monitoring, ECG exercise test and cardiac MRI; cardiac CT was performed only if clinically indicated and in all subjects with >35 years old of age. The type of sport practiced was recorded and stratified according to intensity into low- mid- and high-intensity. The site of negative T waves was recorded and T waves were defined as “deep” if wider than 2 mm. The presence of any arrhythmias during the 24-ECG monitor or exercise ECG test was recorded as well. The primary end-point of the study was the identification of diagnostic criteria for any structural heart disease at cardiac MRI or cardiac CT Results A total of 55 athletes (50 male, 90%) were enrolled with a mean age of 27 ± 14 years-old. Most of them practiced high-intensity sports activity (47 athletes, 85.4%). Anterior T waves were the most common type (29 athletes, 52.7%) and 8 athletes (14.5%) had more than isolated ventricular ectopic beats at 24-hours ECG monitoring. Among the entire cohort, 16 athletes (29.1%) had cardiac MRI or cardiac CT diagnostic for specific structural heart disease. Of interest, the presence of deep negative t waves (OR 8.1 95%CI 1.4–49.5, p<0.001) and arrhythmias more complex than isolated ventricular ectopic beats (OR 5.5 95%CI 1.1–26.6, p<0.001) were significative associated with structural heart disease even in the presence of normal transthoracic echocardiography. Conclusions Our results identified a prevalence of 29% of structural heart disease among athletes with negative T waves at ECG even when transthoracic echocardiography was normal. Of interest deep negative T waves and arrhythmias more complex than isolated ventricular ectopic beats were significative associated with structural heart disease. Thus, according to our results, advanced cardiovascular imaging techniques (cardiac MRI or cardiac CT) should be considered in athletes with negative T waves at ECG even in the presence of normal transthoracic echocardiography especially if complex ventricular arrhythmias of deep negative T waves are present.