腰椎CT画像からの骨梁骨密度予測における深層学習：脊椎骨肥厚症におけるDEXAの優れた代替法

Study DesignRetrospective Cohort Study.ObjectivesThis study explores using deep learning to predict lumbar spine bone mineral density (BMD) from CT images, aiming to improve osteoporosis and osteopenia detection and facilitate early intervention. While dual-energy X-ray absorptiometry (DEXA) is the gold standard, it often overestimates BMD in skeletal hyperostosis. We sought to develop a CT-based model capable of accurately predicting trabecular BMD and validating its fracture-risk prediction performance.MethodsThis retrospective study analyzed 1840 lumbar vertebrae (L1-L4) from 460 patients with available DEXA results. Patients were randomly split 7:3 (patient-level) into training and test cohorts. Trabecular regions were semi-automatically segmented, and based on DEXA, we developed a regression model for predicting BMD values. Additionally, a three-class deep learning model classified normal bone mass, osteopenia, and osteoporosis. Next, we established a BMD-fracture risk prediction model in patients without lumbar osteophytosis or ligamentous calcification and externally validated its performance. In patients with lumbar osteophytosis or ligamentous calcification, we compared fracture-risk prediction between DEXA and model-predicted BMD.ResultsModel-predicted BMD correlated strongly with DEXA (r = 0.934). The three-class model achieved AUCs of 0.90 (normal), 0.79 (osteopenia), and 0.92 (osteoporosis). External validation confirmed that model-predicted BMD (AUC = 0.901) outperformed DEXA (AUC = 0.464) in fracture-risk prediction among hyperostotic patients.ConclusionsDeep learning enabled accurate BMD estimation from lumbar CT images, supporting osteoporosis diagnosis and fracture-risk stratification. Notably, in patients with lumbar osteophytosis or ligamentous calcification, CT-based predicted BMD provided superior risk discrimination compared with DEXA, indicating closer alignment with actual bone status.