Cost Efficient Osteoporosis Analysis using Dental Data


Investigators:
  • Haibin Ling, Ph.D.
    Professor of Computer and Information Sciences
  • Vasileios Megalooikonomou, Ph.D.
    Professor of Computer and Information Sciences
  • Jie Yang, D.M.D.
    Professor of Oral and Maxillofacial Radiology
  • Alan Maurer, M.D.
    Professor of Radiology and Medicine

    Contact:
    Dept. of Computer and Information Sciences
    382 SERC Building, 1925 North 12th St.,
    Philadelphia, PA 19122
    (tel)1-215-204-6973 | (fax)1-215-204-5082
    (email) hbling AT temple.edu

    Supported by:
         
    NSF project link
  •       

    Summary:
    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 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.

    Aside from shifting the bone quality analysis to a new low-cost paradigm, the project can have broad impact on both clinical and computational science. 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 will 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.


    Publications (links to data and code provided whenever applicable)

    Predicting Image Memorability by Multi-view Adaptive Regression
    H. Peng, K. Li, B. Li, H. Ling, W. Xiong, and W. Hu
    ACM Multimedia Conference (ACM MM), 2015.
    PDF
    Cross gender-age trabecular texture analysis in dental cone beam computed tomography
    H. Ling, X. Yang, P. Li, V. Megalooikonomou, Y. Xu, and J. Yang
    Dentomaxillofacial Radiology (DMFR), 43:20130324, 2014
    PDF
    Trabecular Texture Analysis in Dental CBCT by Multi-ROI Multi-Feature Fusion
    P. Li, X. Yang, F. Xie, J. Yang, E. Cheng, V. Megalooikonomou, Y. Xu, and H. Ling
    Proc. of IEEE Int'l Symposium on Biomedical Imaging (ISBI), 2014
    PDF