SCH: EXP: Cost Efficient Osteoporosis Analysis using Dental Data

该项目使用牙科数据调查低成本的骨质疏松症预筛查方法，这些牙科数据是在常规牙科检查期间收集的，因此不需要额外费用。特别是，当老年人到牙科诊所进行常规治疗时，建议的方法根据收集的数据(如牙科X光照片)评估骨质疏松症的证据。如果发现高风险，老年人将被推荐进行正式的骨质疏松检查。为此，该项目开展了三项主要研究活动，包括系统验证牙科数据与骨骼质量测量之间的关系，基于牙科图像的骨质疏松分析，以及整合纵向和分类信息进行骨质疏松预筛查。骨骼质量的下降在美国造成了主要的健康问题。特别是，据估计，在50岁及以上的美国人中，有55%患有骨质疏松症。骨质疏松症的早期诊断需要常规检查，因为在严重后果(如骨折)发生之前，没有明显的症状与诊断相关。这种常规检查可能会造成很大的经济负担，因为目前的黄金标准(即双能X射线吸收法)使用的数据收集起来并不划算。该项目开发了使用牙齿数据的低成本骨质疏松预筛查方法的图像分析和机器学习方法。这项研究在计算和临床领域都推动了科学的发展。特别是，它是使用常规收集的牙齿数据进行低成本智能健康评估的示范模型。此外，该项目中开发或发明的特定技术可以很容易地推广到其他相关的临床和非临床领域。此外，数据分析算法可在许多科学和工程领域引起普遍兴趣，如计算机视觉、医学图像分析、数据挖掘、气候进化等。该项目的教育活动与研究活动紧密结合，通过培训和教授不同水平的学生，向普通受众传播研究成果，并让代表不足的学生参与研究。

This project investigates low-cost osteoporosis prescreening methods using dental data, which are collected during routine dental examination and thus at no additional cost. In particular, when a senior citizen attends the dental office for routine treatment, the proposed methods assess the evidence of osteoporosis based on collected data such as dental radiographs. The senior citizen is referred to a formal osteoporotic examination if high risk is found. Towards this goal, the project conducts three major research activities including systematical validation of the relation between dental data and bone quality measurement, dental image-based osteoporosis analysis, and integration of longitudinal and categorical information for osteoporosis prescreening. Decrease in bone quality causes major health problems in the United States. In particular, it has been estimated that osteoporosis afflicts 55% of Americans aged 50 and above. Early diagnosis of osteoporosis requires routine examination since no obvious symptom is associated with diagnosis before serious consequences, e.g., bone fracture, happen. Such routine examination can cause a big economic burden, since the data used in the current gold standard (i.e., dual energy X-ray absorptiometry) is not cost efficient to collect. This project develops image analysis and machine learning methods for low-cost osteoporosis prescreening methods using dental data. The research advances science in both computational and clinical fields. In particular, it serves as an exemplary model of using routinely collected dental data for low-cost smart health assessment. Moreover, the specific techniques exploited or invented in this project can be easily generalized to other related clinical and non-clinical domains. In addition, the data analytics algorithms can be of general interest in many areas of science and engineering such as computer vision, medical image analysis, data mining, climate evolution, etc. The education activities of the project are tightly integrated with the research activities, by training and teaching students of different levels, disseminating research results to general audience, and involving under-represented students in research.