SCH: EXP: Cost Efficient Osteoporosis Analysis using Dental Data

SCH：EXP：使用牙科数据进行成本效益的骨质疏松症分析

• 批准号: 1407156
• 负责人: Haibin Ling
• 金额: $ 59.58万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1407156&HistoricalAwards=false
• 关键词: SCH; EXP; Cost; Efficient; Osteoporosis

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.

该项目利用牙科数据研究低成本的骨质疏松症预筛查方法，这些数据是在常规牙科检查期间收集的，因此无需额外费用。特别是，当老年人到牙科诊所接受常规治疗时，所提出的方法会根据收集的数据（例如牙科射线照片）评估骨质疏松症的证据。如果发现高风险，老年人将被转介接受正式的骨质疏松检查。为了实现这一目标，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质量测量之间的关系、基于牙科图像的骨质疏松症分析以及整合纵向和分类信息以进行骨质疏松症预筛查。在美国，骨质量下降会导致严重的健康问题。特别是，据估计 55% 的 50 岁及以上美国人患有骨质疏松症。骨质疏松症的早期诊断需要常规检查，因为在发生骨折等严重后果之前，没有明显的症状与诊断相关。这种例行检查可能会造成巨大的经济负担，因为当前金标准（即双能 X 射线吸收测定法）中使用的数据收集起来并不具有成本效益。该项目开发图像分析和机器学习方法，用于使用牙科数据的低成本骨质疏松症预筛查方法。该研究推动了计算和临床领域的科学发展。特别是，它可以作为使用常规收集的牙科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具体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，数据分析算法可以在计算机视觉、医学图像分析、数据挖掘、气候演化等许多科学和工程领域引起人们的普遍兴趣。该项目的教育活动与研究活动紧密结合，通过培训和教学不同水平的学生，向普通受众传播研究成果，并让代表性不足的学生参与研究。