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.
