Adolescent idiopathic scoliosis (AIS) often presents with significant 3D asymmetry of the torso, posing challenges for both patients and clinicians. Surface topography, based on the identification of anatomical landmarks, offers a non-invasive alternative to x-rays for monitoring shape changes over time, thereby reducing radiation exposure. However, the current gold standard, a manual landmarking process, is labor intensive and prone to error. Here, we present an automated 3D landmark digitization method designed to address these limitations. We performed the validation comparing the automated 3D landmark digitization method against the manual gQ1old standard across three phases: preliminary error assessment, geometric morphometrics (GMM) shape/size evaluation, and practical allometry application. Our results show that the automated method effectively quantifies torso shape, achieving a nonsignificant measurement error in both groups (23.1 mm in patients with p-value = 0.33; and 20.3 mm in controls with p-value = 0.30). It has also captured variance patterns comparable to the manual approach, showing high agreement for PC1 (0.94; CI95%: 0.91–0.96) and good agreement for PC2 (0.85; CI95%: 0.78–0.90), and performs similarly in assessing allometry, without significant differences in capturing the allometric signal (p-value = 0.09). However, the automated method exhibited reduced ability to capture shape variability, highlighting potential areas for improvement. These results suggest that automated, non-radiographic techniques hold promise for clinical application in tracking AIS progression. Future refinements could further improve accuracy, paving the way for safer and more efficient scoliosis management strategies.
Bastir et al. (2026) studied this question.