Cardio-metabolic risk modeling and assessment through sensor-based measurements. (September 2022) - Giorgi, Daniela; Bastiani, Luca; Morales, Maria Aurora; Pascali, Maria Antonietta; Colantonio, Sara; Coppini, Giuseppe
Highlights: Smart devices open new ways to assess cardiometabolic risk in daily life. We define a model based on anthropometry, glycolipid factors, and vascular function. Measurements of model variables are taken on a subject face using the Wize Mirror system. SOM networks can detect subjects at-risk in good agreement with clinical evaluation. Abstract: Objective: Cardio-metabolic risk assessment in the general population is of paramount importance to reduce diseases burdened by high morbility and mortality. The present paper defines a strategy for out-of-hospital cardio-metabolic risk assessment, based on data acquired from contact-less sensors. Methods: We employ Structural Equation Modeling to identify latent clinical variables of cardio-metabolic risk, related to anthropometric, glycolipidic and vascular function factors. Then, we define a set of sensor-based measurements that correlate with the clinical latent variables. Results: Our measurements identify subjects with one or more risk factors in a population of 68 healthy volunteers from the EU-funded SEMEOTICONS project with accuracy 82.4 %, sensitivity 82.5 %, and specificity 82.1 % . Conclusions: Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention.