Artificial Intelligence Techniques as Potential Tools for Large Scale Surveillance and Interventions for Obesity

Alice Guo, Min Jiang
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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.
人工智能技术作为大规模监测和干预肥胖的潜在工具
肥胖与糖尿病是两种代谢性疾病,两者之间有着密切的关系。肥胖的诊断和监测对公共卫生管理、政策制定和干预措施至关重要。目前的做法主要是通过个人到医院或诊所进行肥胖和糖尿病的测量和诊断,或者通过电话和个人访谈进行监测。我们主张,随着人工智能(AI)的进步,利用人工智能技术进行肥胖诊断和监测具有巨大的潜力。关键方法是利用相机传感器拍摄人脸或人体的照片,对照片进行计算分析,获得身体质量指数(BMI)估算。这些人工智能技术使大规模诊断和监测公共卫生状况成为可能。此外,在互联网连接和智能电话的帮助下,这些技术还使对大量人口的干预成为可能。在这篇文章中,上述的想法是通过对当前可用的人工智能技术的简要概述和总结来提出的,为一种创新的方式来进行肥胖的诊断、监测和干预打开了一扇窗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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