PROJECT SUMMARY/ABSTRACT
Low bone mineral density (BMD) is an innocuous and pernicious disease that eventually results in osteopenia
and osteoporosis, and increased risk for osteoporotic fractures. These fractures result in increased morbidity
due to pain, decreased locomotion, loss of income, and increased healthcare cost. Hip fractures have been
associated with increased mortality, with up to 35% 3-year mortality rate. Unfortunately, most patients at risk
for fractures are not screened for low BMD using dual energy x-ray absorptiometry (DXA). There is a need to
identify patients with low BMD to institute therapy/treatment to prevent the development of fractures.
Approximately 280 million radiographs are performed annually in the United States. Our central hypothesis is
that deep learning analysis of these radiographs could provide information about the underlying patient bone
quality and predict whether a patient is osteoporotic or osteopenic. The long-term goals are (1) to identify
imaging biomarkers (radiomic features) that are associated with osteoporosis and osteopenia and (2) to
identify imaging biomarkers that predict underlying trabecular bone scores (TBS). This proposal utilizes cutting
edge computational and statistical approaches based on radiographs of patients with concurrent DXA studies.
The central hypothesis will be tested by pursuing three specific aims: (1) determine key reproducible imaging
biomarkers (radiomic features) of bones from radiographs in patients without implanted metallic devices that
could be used to reliably predict whether a patient has osteoporosis or osteopenia; (2) identify imaging
biomarkers (radiomic features) of bones from radiographs in patients with implanted metallic devices that could
be used to reliably predict whether a patient has osteoporosis or osteopenia; (3) determine whether these
imaging biomarkers from radiographs could be used to predict the bone TBS. These aims will be pursued
using novel computational and statistical techniques from the imaging sciences. We will identify imaging
features from radiographs that can predict whether a patient has osteoporosis or osteopenia and who should
go on to further screening with DXA, which would be a significant advancement for clinical healthcare. We also
aim to identify imaging features that can predict TBS, which provides additional information about the bone
microarchitecture and future fracture risk. The impact of this work is that we will have a better understanding of
the manifestations of osteoporosis and osteopenia from radiographs. The results of this work will identify
patients who have undergone radiographs who should go on to have formal DXA evaluation because of their
increased risk for osteoporosis and osteopenia.
