LAUSR

BACKGROUND Scoliosis is spinal curvature that may progress to require surgical stabilisation. Risk factors for progression are little understood due to lack of population-based research, since radiographs cannot be performed on entire populations due to high levels of radiation. To help address this, we have previously developed and validated a method for quantification of spinal curvature from total body dual energy X-ray absorptiometry (DXA) scans. The purpose of this study was to automate this quantification of spinal curve size from DXA scans using machine learning techniques. METHODS To develop the automation of curve size, we utilised manually annotated scans from 7298 participants from the Avon Longitudinal Study of Parents and Children (ALSPAC) at age 9 and 5122 at age 15. To validate the automation we assessed (1) agreement between manual vs automation using the Bland-Altman limits of agreement, (2) reliability by calculating the coefficient of variation, and (3) clinical validity by running the automation on 4969 non-annotated scans at age 18 to assess the associations with physical activity, body composition, adipocyte function and backpain compared to previous literature. RESULTS The mean difference between manual vs automated readings was less than one degree, and 90.4 % of manual vs automated readings fell within 10°. The coefficient of variation was 25.4 %. Clinical validation showed the expected relationships between curve size and physical activity, adipocyte function, height and weight. CONCLUSION We have developed a reasonably accurate and valid automated method for quantifying spinal curvature from DXA scans for research purposes.