Deep learning applied to facial features for prediction of high risk coronary atherosclerosis at cardiac CT: preliminary data from LOMBROSO Trial
Authors: Conte E , Chiesa M , Mushtaq S , Schillaci M , Marchetti D , Baggiano A , Volpe A , Colombo G , Pompilio G , Bartorelli A , Pontone G , Andreini D 
Published: October 2022

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 &lt;150 HU at CT); the non-calcified plaque volume was quantified on a per-patient basis in mm3 and a cut off of &gt;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.