Automated CT dataset generation as a novel concept for verification of backshape-to-spine approach and Cobb Angle Estimation

Background: Adolescent idiopathic scoliosis is a condition that affects up to 3% of adolescents. Although radiographic imaging is the gold standard for diagnosis and monitoring, repeated exposure to ionizing radiation poses significant health risks. Alternative methods, such as estimating spinal alignment and Cobb angle from back surface topography, have been proposed to mitigate these risks. However, validation of these methods is challenging because traditional datasets combine back surface scans and radiographs acquired at different times and in different postures. This introduces technique-dependent errors and makes it difficult to determine whether observed discrepancies stem from the method itself or from inconsistencies in the validation dataset. As a result, this uncertainty in methods accuracy arising from verification errors, limits these alternatives to complementary roles rather than establishing them as reliable diagnostic tools. Methods: To address this limitation, we introduce a fully automated approach for generating verification datasets directly from computed tomography images. This approach enables precise and synchronized extraction of both external back surface and internal spinal structures, thereby eliminating posture-related discrepancies present in commonly used datasets. We assess the utility of our method through a two-step validation: first, by comparing automatically extracted parameters with manual measurements; and second, by comparing the results of an established surface topography method applied to our dataset with reported values from the literature. Results: Our findings suggest that this automatically extracted CT dataset serves as a suitable reference standard for the verification and comparison of surface topography approaches. Conclusion: This work provides a robust framework for future verification studies of different surface topography methods and may facilitate the development of non-radiographic techniques for scoliosis assessment, thereby reducing reliance on ionizing radiation in clinical practice.