Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners

Abstract

Abstract This retrospective study examined the diagnostic efficacy of automated deep learning-based bone mineral density (DL-BMD) measurements for osteoporosis screening using 422 CT datasets from four vendors in two medical centers, encompassing 159 chest, 156 abdominal, and 107 lumbar spine datasets. DL-BMD values on L1 and L2 vertebral bodies were compared with manual BMD (m-BMD) measurements using Pearson’s correlation and intraclass correlation coefficients. Strong agreement was found between m-BMD and DL-BMD in total CT scans (r = 0.953, p < 0.001). The diagnostic performance of DL-BMD was assessed using receiver operating characteristic analysis for osteoporosis and low BMD by dual-energy x-ray absorptiometry (DXA) and m-BMD. Compared to DXA, DL-BMD demonstrated an AUC of 0.790 (95% CI 0.733–0.839) for low BMD and 0.769 (95% CI 0.710–0.820) for osteoporosis, with sensitivity, specificity, and accuracy of 80.8% (95% CI 74.2–86.3%), 56.3% (95% CI 43.4–68.6%), and 74.3% (95% CI 68.3–79.7%) for low BMD and 65.4% (95% CI 50.9–78.0%), 70.9% (95% CI 63.8–77.3%), and 69.7% (95% CI 63.5–75.4%) for osteoporosis, respectively. Compared to m-BMD, DL-BMD showed an AUC of 0.983 (95% CI 0.973–0.993) for low BMD and 0.972 (95% CI 0.958–0.987) for osteoporosis, with sensitivity, specificity, and accuracy of 97.3% (95% CI 94.5–98.9%), 85.2% (95% CI 78.8–90.3%), and 92.7% (95% CI 89.7–95.0%) for low BMD and 94.4% (95% CI 88.3–97.9%), 89.5% (95% CI 85.6–92.7%), and 90.8% (95% CI 87.6–93.4%) for osteoporosis, respectively. The DL-based method can provide accurate and reliable BMD assessments across diverse CT protocols and scanners.

Keywords

• Bone density
• Deep learning
• Osteoporosis
• Tomography
• X-Ray computed