Development of Deep Learning Artificial Intelligence to Detect Osteoporosis

Osteoporosis poses a substantial social and economic burden, with estimated treatment costs reaching a combined six trillion USD in the USA, Canada, and Europe. Although dual-energy X-ray absorptiometry (DEXA) is the diagnostic gold standard, computed tomography (CT) scans have proven to be reliable proxies for bone density measurement. Opportunistic screening for low bone density using CT obtained for other purposes can potentially reduce complications from osteoporotic fractures and health care costs. In this study, we developed an artificial intelligence (AI) algorithm using neural networks and the MONAI library to estimate DEXA bone density from non-contrast cardiac CT obtained for coronary calcium scoring purposes. A total of 2797 Dallas Heart Study phase 2 participants (39% male, 61% female) were included. The AI algorithm was first developed to automatically segment trabecular bone from cortical bone. This was trained and validated with manual segmentation of the trabecular bone by two medical students, a radiologist, using MONAI 3D autoseg. The ML algorithm achieved a Dice score of 0.97 when compared to human segmentation. A second AI model was developed utilizing segmentations of the first model. This AI was trained utilizing corresponding DEXA bone mineral density (BMD) for thoracic vertebrae. The best performing model was trained for 102 epochs, resulting in a training root mean square error (RMSE) of 0.0628 mg/cm2 and validation RMSE of 0.0842 mg/cm2. The final AI algorithm predictions yielded an R2 value of 0.71 compared to DEXA (Figure 1). Our findings underscore the clinical feasibility for an automated neural network to predict DEXA scores from non-contrast cardiac CT. This approach may help in the early detection of unsuspected low bone mineral density in patients undergoing CT scans for other reasons, allowing for potential improvements in patient outcomes and resourceful utilization of diagnostic imaging.