Deep Learning-based Opportunistic CT Osteoporosis Screening and the Establishment of Normative Values

Background Osteoporosis is underdiagnosed and undertreated, prompting the exploration of opportunistic screening using CT and artificial intelligence. Purpose To develop a reproducible convolutional neural network to automatically identify a three-dimensional (3D) region of interest (ROI) in trabecular bone, develop a correction method to normalize attenuation values across different CT protocols and scanner models, and establish thresholds for diagnosing osteoporosis in a large diverse population. Materials and Methods In this retrospective study, a deep learning-based method was developed to automatically quantify trabecular attenuation of the thoracic and lumbar spine on CT images with use of a 3D ROI. A statistical method was developed to adjust for different tube voltages and scanner models. Normative values and diagnostic thresholds for trabecular attenuation of the spine for osteoporosis were established based on the reported prevalence of osteoporosis by the World Health Organization. Differences between groups were assessed using the Student t test. Results A total of 538 946 CT examinations from 283 499 patients (mean age, 65 years ± 15 [SD]; 145 021 [51.2%] female; 157 457 [55.5%] White patients) were analyzed, representing 43 scanner models and six different tube voltages. The attenuation values at 80 kVp and 120 kVp differed by 23%, and different scanner models resulted in differences in values of less than 10%. The automated ROI placement of 1496 vertebrae was validated by manual radiologist review and demonstrated greater than 99% agreement. Trabecular attenuation was greater in young women (age <50 years) than in young men (P < .001) and decreased with age, with a steeper decline in postmenopausal women. In patients older than 50 years, trabecular attenuation was greater in male than in female patients (P < .001). Trabecular attenuation was highest in Black patients, followed by Asian patients, and lowest in White patients (P < .001). Conclusion Deep learning-based automated opportunistic osteoporosis screening can identify patients with low bone mineral density using CT scans obtained for clinical purposes with use of different scanners and protocols. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Feuerriegel and Sutter in this issue.