Automated opportunistic screening for low bone mineral density using routine CT brain imaging.

OBJECTIVES: CT brain (CTB) scans are frequently performed in older adults, a population at increased risk of osteoporosis and low bone mineral density (BMD), presenting an opportunity for opportunistic screening. This study aims to evaluate an automated deep learning approach for opportunistic screening of low BMD and osteoporosis from routine CTB imaging. MATERIALS AND METHODS: A single-centre retrospective analysis was conducted on 2,014 patients (mean age 69.7 ± 14.9 years; 61% female) who underwent non-contrast CTB and dual-energy x-ray absorptiometry (DEXA) within one year of each other. A convolutional neural network incorporating automatically selected CTB slices cranial to the lateral ventricles, as well as age and sex, was trained to perform two binary classification tasks: low BMD screening (T-score < -1.0) and osteoporosis screening (T-score ≤ -2.5). Model performance was evaluated on a 10% hold-out test set using AUC, balanced accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, with subgroup analysis by sex. RESULTS: 22% of patients scanned had normal BMD, 44% had osteopenia, and 34% had osteoporosis. For low BMD screening, the model achieved an AUC of 0.83 (95% CI: 0.76-0.90), with AUCs of 0.88 in females and 0.76 in males. For osteoporosis screening, the model achieved an AUC of 0.78 (95% CI: 0.72-0.85), with AUCs of 0.78 in females and 0.74 in males. CONCLUSION: Automated analysis of routine CT brain imaging showed good discriminatory performance for opportunistic low BMD screening, particularly among females. With further validation, this approach could support earlier identification of at-risk individuals using imaging already acquired in routine clinical care.