Abstract

Obesity and diabetes are two metabolic disorder diseases, which are strictly correlated. The diagnosis and surveillance of obesity is crucial for public health management, policy making, and interventions. Current practices are mainly based on individuals’ visits to hospitals or clinics to get the measurement and diagnosis for obesity and diabetes, or with telephone calls and personal interviews for surveillance. We advocate that with advances in artificial intelligence (AI), there is great potential to perform obesity diagnosis and surveillance with AI technologies. The key approaches are based on taking pictures or photos of human faces or bodies by using camera sensors, performing computational analysis of the photos, and obtaining the body mass index (BMI) estimation. These AI technologies make it possible to accomplish a large scale diagnosis and monitoring of public health conditions. Furthermore, these technologies also make it possible for interventions with large populations, aided by Internet connections and smart phones for communications. In this article, the aforementioned idea is presented with a brief overview and summary of the currently available AI technologies, opening a window for an innovative way to perform diagnosis, surveillance, and interventions for obesity.

Keywords: Obesity; Diagnosis; Body mass index; Surveillance; Interventions

Abbreviations: AI: Artificial Intelligence; BMI: Body Mass Index; SVR: Support Vector Regression; GPR: Gaussian Process Regression; CJWR: Cheek-to-Jaw-Width Ratio; WHR: Face Width-to-Height Ratio; PAR: Face Perimeter to Area Ratio