Advancing Osteoporosis Opportunistic Screening: Multicenter Validation of a Deep Learning Algorithm Using Abdominal CT Scans

Purpose To develop and do multicenter validation on an algorithm that screens for osteoporosis from abdominal CTs. Methods This is a diagnostic accuracy study with retrospective data from January 2022 to July 2022 consisting of two steps: a segmentation step of the lumbar vertebral bodies, involving outpatient non-contrast abdominal CTs from [ANONYMIZED], and a multicenter validation step incorporating data from four additional institutions. The segmentation employed a 2D UNet with a ResNet34 backbone. We determined the Pearson correlation coefficient (r) between the mean of the slices’ mean attenuations (MSMA) on CT scans against the bone mineral density (BMD) on recent DEXA scans and calculated performance metrics for osteoporosis prediction, including 95% confidence intervals. Results The multicenter validation included 504 participants (median age, 66 years, interquartile range, 56–72; 388 women). A linear regression analysis showed an r of 0.63 (95% CI, 0.57–0.68) between MSMA (HU) and BMD (g/cm²). The AUCs (95% CI) for distinguishing between normal and osteoporosis were 0.96 (0.89, 1.0) for the internal dataset and 0.82 (0.75, 0.89) for the external dataset, and the performance metrics (95% CI), for a threshold of 202.6 HU, were 100% sensitivity (94, 100) and 91% specificity (84, 95) for internal data and 79% sensitivity (61, 90) and 81% specificity (76, 84) for external sites. Conclusion We developed and performed a multicenter validation of a DL model for osteoporosis prediction on CT.