The development of lumbar lordosis has been traditionally examined using angular measurements of the spine to reflect its shape. While studies agree regarding the increase in the angles during growth, the growth rate is understudied, and sexual dimorphism is debated. In this study, we used a novel method to estimate the shape of the lumbar curve (LC) using the landmark-based geometric morphometric method to explore changes in LC during growth, examine the effect of size and sex on LC shape, and examine the associations between angular measurements and shape. The study population included 258 children aged between 0 and 20 years (divided into five age groups) who underwent a CT scan between the years 2009 and 2019. The landmark-based geometric morphometric method was used to capture the LC shape in a sagittal view. Additionally, the lordosis was measured via Cobb and sacral slope angles. Multivariate and univariate statistical analyses were carried out to examine differences in shape between males and females and between the age groups.Aims
Methods
To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology.Aims
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