Deep learning applied to facial features for prediction of high risk coronary atherosclerosis at cardiac CT: preliminary data from LOMBROSO Trial

Abstract

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.

Reactive Oxygen Species-Responsive Sequentially Targeted AIE Fluorescent Probe for Precisely Identifying the Atherosclerotic Plaques

“…34 Additionally, the microenvironment of atherosclerotic plaques is rich in reactive oxygen species (ROS), which can be utilized as the trigger for controlling cargo release. 11 Based on the above biological targets and unique biopathological environment, various nanoagents have been engineered as multifunctional nanoprobes or stimuliresponsive nanoplatforms to achieve precise imaging or therapy of atherosclerotic plaques.…”

“…Currently, computed tomography (CT), optical coherence tomography (OCT), magnetic resonance imaging (MRI), and ultrasonography (US) are utilized to detect the structural and anatomic features of atherosclerotic plaques . However, these conventional methods always have difficulty in providing the detailed information for atherosclerotic lesions owing to their intrinsically low spatial resolution .…”

“…A PS-specific peptide with an amino acid sequence of CLIKKPF (CF) has been demonstrated to bind to membranes containing externalized PS of apoptotic cells, which have high affinity and specificity for PS . Additionally, the microenvironment of atherosclerotic plaques is rich in reactive oxygen species (ROS), which can be utilized as the trigger for controlling cargo release . Based on the above biological targets and unique biopathological environment, various nanoagents have been engineered as multifunctional nanoprobes or stimuli-responsive nanoplatforms to achieve precise imaging or therapy of atherosclerotic plaques.…”

“…8,9 Thence, it is highly important to efficiently recognize the foam cells and then precisely identify the morphology and disperse distribution of the plaques for physicians to make timely clinical decisions and perform proactive interventions as early as possible. 10 Currently, computed tomography (CT), 11 optical coherence tomography (OCT), 12 magnetic resonance imaging (MRI), 13 and ultrasonography (US) 14 are utilized to detect the structural and anatomic features of atherosclerotic plaques. 6 However, these conventional methods always have difficulty in providing the detailed information for atherosclerotic lesions owing to their intrinsically low spatial resolution.…”