Opportunistic screening for osteoporosis can be performed using low‐dose computed tomography (LDCT) imaging obtained for other clinical indications. In this study we explored the CT‐derived bone mineral density (BMD) and prevalence of osteoporosis from thoracic LDCT in a large population cohort of Chinese men and women. A total of 69,095 adults (40,733 men and 28,362 women) received a thoracic LDCT scan for the purpose of lung cancer screening between 2018 and 2019, and data were obtained for analysis from the China Biobank Project, a prospective nationwide multicenter population study. Lumbar spine (L1–L2) trabecular volumetric bone mineral density (vBMD) was derived from these scans using quantitative computed tomography (QCT) software and the American College of Radiology QCT diagnostic criteria for osteoporosis were applied. Geographic regional differences in the prevalence of osteoporosis were assessed and the age‐standardized, population prevalence of osteoporosis in Chinese men and women was estimated from the 2010 China census. The prevalence of osteoporosis by QCT for the Chinese population aged >50 years was 29.0% for women and 13.5% for men, equating to 49.0 million and 22.8 million, respectively. In women, this rate is comparable to estimates from dual‐energy X‐ray absorptiometry (DXA), but in men, the prevalence is double. Prevalence varied geographically across China, with higher rates in the southwest and lower rates in the northeast. Trabecular vBMD decreased with age in both men and women. Women had higher peak trabecular vBMD (185.4 mg/cm3) than men (176.6 mg/cm3) at age 30 to 34 years, but older women had lower trabecular vBMD (62.4 mg/cm3) than men (92.1 mg/cm3) at age 80 years. We show that LDCT‐based opportunistic screening could identify large numbers of patients with low lumbar vBMD, and that future cohort studies are now required to evaluate the clinical utility of such screening in terms of fracture prevention and supporting national health economic analyses. © 2020 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)..

Objective Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was developed to automatically measure bone mineral density (BMD) for opportunistic osteoporosis screening using lowdose chest computed tomography (LDCT) scans obtained for lung cancer screening. Methods First, a deep learning model was trained and tested with 200 annotated LDCT scans to segment and label all vertebral bodies (VBs). Then, the mean CT numbers of the trabecular area of target VBs were obtained based on the segmentation mask through geometric operations. Finally, a linear function was built to map the trabecular CT numbers of target VBs to their BMDs collected from approved software used for osteoporosis diagnosis. The diagnostic performance of the developed system was evaluated using an independent dataset of 374 LDCT scans with standard BMDs and osteoporosis diagnosis. Results Our deep learning model achieved a mean Dice coefficient of 86.6% for VB segmentation and 97.5% accuracy for VB labeling. Line regression and Bland-Altman analyses showed good agreement between the predicted BMD and the ground truth, with correlation coefficients of 0.964-0.968 and mean errors of 2.2-4.0 mg/cm 3. The area under the curve (AUC) was 0.927 for detecting osteoporosis and 0.942 for distinguishing low BMD. Conclusion The proposed deep learning-based system demonstrated the potential to automatically perform opportunistic osteoporosis screening using LDCT scans obtained for lung cancer screening. Key Points • Osteoporosis is a prevalent but underdiagnosed condition that can increase the risk of fracture. • A deep learning-based system was developed to fully automate bone mineral density measurement in low-dose chest computed tomography scans. • The developed system achieved high accuracy for automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening. Keywords Bone mineral density. Deep learning. Osteoporosis. Screening Abbreviations AUC Area under the curve BMD Bone mineral density CNN Convolutional neural network CT Computed tomography DL Deep learning DXA Dual-energy X-ray absorptiometry LDCT Low-dose chest computed tomography QA Quality assurance QCT Quantitative computed tomography VB Vertebral body VOI Volume of interest Yaling Pan and Dejun Shi contributed equally to this work.

Objectives To automatically measure the Cobb angle and diagnose scoliosis on chest X-rays, a computer-aided method was proposed and the reliability and accuracy were evaluated. Methods Two Mask R-CNN models as the core of a computer-aided method were used to separately detect and segment the spine and all vertebral bodies on chest X-rays, and the Cobb angle of the spinal curve was measured from the output of the Mask R-CNN models. To evaluate the reliability and accuracy of the computer-aided method, the Cobb angles on 248 chest X-rays from lung cancer screening were measured automatically using a computer-aided method, and two experienced radiologists used a manual method to separately measure Cobb angles on the aforementioned chest X-rays. Results For manual measurement of the Cobb angle on chest X-rays, the intraclass correlation coefficients (ICC) of intra-and inter-observer reliability analysis was 0.941 and 0.887, respectively, and the mean absolute differences were < 3.5°. The ICC between the computer-aided and manual methods for Cobb angle measurement was 0.854, and the mean absolute difference was 3.32°. These results indicated that the computer-aided method had good reliability for Cobb angle measurement on chest X-rays. Using the mean value of Cobb angles in manual measurements > 10° as a reference standard for scoliosis, the computer-aided method achieved a high level of sensitivity (89.59%) and a relatively low level of specificity (70.37%) for diagnosing scoliosis on chest X-rays. Conclusion The computer-aided method has potential for automatic Cobb angle measurement and scoliosis diagnosis on chest X-rays.