A striking deficiency in the diagnosis of osteoporosis, a disease disproportionately afflicting elderly women, is a suitable mass screening tool for its detection. Over ten million women are affected in the U.S. at a cost exceeding seven billion dollars annually. Typically, osteoporosis goes undetected until pathologic bony fractures occur after a long period of asymptomatic bone loss. Radiographic changes include a generalized rarefaction of bone with various patterns of trabecular and cortical resorption. Bones that are highly trabecular tend to be affected earliest and those with a great amount of trabeculation and thin cortices provide for the earliest radiographic signs of decalcification. Based upon preliminary studies we hypothesize that dental radiographs, routinely taken throughout life, can be digitally processed to provide early detection of osteoporosis. The broad long term goal is to develop a screening tool for osteoporosis using standardized conventional dental periapical radiographs.
The specific aims of this proposed research are: 1) to identify and evaluate digital image features which reveal radiographic changes consistent with alveolar bone density loss using in vitro techniques to simulate osteoporosis, 2) to determine the robustness of these features to radiographic technique variation, 3) to derive the optimal set of image features for detecting a minimal loss of bone (for each region of the maxilla and mandible) and for accurately estimating the degree of bone density loss from a longitudinal series of radiographs. That is, to establish a quantifiable relationship between in vitro alveolar bone density loss and the change in magnitude;, of the optimal set of image features. Sections of human maxillary and mandibular alveolar bone will be decalcified and conventional radiographs will be made of the sections at selected stages of decalcification. The radiographs will be digitized and interproximal bony scan lines, or profiles, will be generated. Digital image enhancement processing of the profiles will be studied followed by an exhaustive application of image feature extraction algorithms. After discarding features which are highly sensitive to radiographic technique variation, regression analysis will be used to generate a predictive equation relating in vitro alveolar bone density loss to the change in magnitude of digitized radiographic features. The proposed research will establish a foundation for developing a clinical screening tool for osteoporosis. We foresee the development of such a tool as providing dentistry a pivotal role in the early detection of osteoporosis. Such detection will reduce the morbidity and mortality of these afflictions.
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