Artificial intelligence analysis applied to facial features for prediction of high-risk coronary atherosclerosis at cardiac CT: the LOMBROSO Trial

Abstract

Abstract Background Cardiac computed tomography (CT) has recently emerged as an accurate tool for non-invasive evaluation of coronary artery disease (CAD) and coronary atheroscelrosis itself. 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 at cardiac CT. Methods We enrolled a consecutive cohort of 100 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. 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 80 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 the final results from the first 100 patients enrolled (69 male and 31 female, with mean age of 62±10 years old). In 30 patients cardiac CT resulted completely normal, while in 70 subjects the presence of coronary atherosclerosis was demonstrated. On the test set, we obtained for deep learning facial analysis an AUC equal to 0.79, 1, 0.94, 0.89 for the prediction of presence of high risk plaque, elevated low density plaque volume and elevated non-calcific plaque volume. These AUC data were significatively higher when compared to SCORE2 capability to predict the presence of high risk plaque features, low density plaque volume and non-calcific plaque volume (AUC equal to 0.65, 0.64, 0.66, respectively) among all 100 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. Of note, significatively high AUC were demonstrated for deep learning facial analysis when compared to SCORE2. 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.