Keywords: Bone/Skeletal, Machine Learning/Artificial Intelligence, AutoML, Radiomics, Osteoporosis, MRI
Motivation: To develop a machine learning model utilizing demographic data and lumbar fat-water MRI as an opportunistic screening for osteoporosis.
Goal(s): To compare the performance of AutoML in predicting osteoporosis using demographic features, radiomics from lumbar fat and water MRI, and their combination.
Approach: A TPOP-radiomics-based classification model was trained using demographic features and radiomic data from lumbar fat-water MRI, differentiating between normal and osteoporosis as identified by DEXA.
Results: The combined model emerged as the most effective from the AutoML process, achieving a mean sensitivity of 0.783 and a mean specificity of 0.867 in distinguishing between normal and osteoporosis.
Impact: Because osteoporosis is often considered a 'silent' disease, routine IDEAL-IQ lumbar scans have the potential to serve as an opportunistic screening tool for reducing the risk of fragility fractures, which are associated with morbidity and mortality.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords