Purpose
To evaluate the association between lumbar lordosis and age using an AI-based automated measurement model applied to a large dataset of standing lateral spinal radiographs.
Materials and Methods
This retrospective study analyzed 904 high-quality radiographs selected from 2,397 images acquired between 2019 and 2021. Lumbar lordosis was defined as the angle between the superior endplates of L1 and S1 and automatically measured using a validated deep learning model. Subjects were categorized into nine age groups. One-way ANOVA compared lumbar lordosis across age groups, and Pearson correlation assessed the relationship between age and lumbar lordosis.
Results
Lumbar lordosis ranged from 0° to 84° (mean 45.9°±13.4°). The highest mean value was in the 10–19-year group (52.1°), and the lowest in the ≥80-year group (39.6°). Minimum values decreased to 0° in individuals aged ≥60 years. No significant differences were found across age groups (p=0.561). A weak but significant negative correlation was observed between age and lumbar lordosis (r=–0.247, p<0.0001).
Conclusions
AI-based automated measurement enabled efficient large-scale analysis and revealed a wide distribution of lumbar lordosis with a gradual age-related decline. These findings highlight the value of AI in spinal alignment assessment.

Advanced imaging technologies have revolutionized the diagnosis and management of spinal pathologies by providing superior precision and efficiency. Modalities such as PET-CT, SPECT, diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS) offer unique insights into the metabolic, structural, and functional aspects of spinal diseases, enabling better differentiation of lesions, improved surgical planning, and early detection of pathological changes. Furthermore, the integration of artificial intelligence (AI) has enhanced imaging workflows by enabling automated analysis, prediction of clinical outcomes, and segmentation of spinal structures. Despite these advancements, challenges such as technical limitations, high costs, and ethical concerns, including issues of data privacy and AI-generated inaccuracies, hinder widespread adoption. This review explores the clinical applications, limitations, and future directions of these emerging technologies, highlighting the need for multidisciplinary collaboration and large-scale research to standardize protocols and optimize patient outcomes. The seamless integration of advanced imaging and AI represents a transformative potential for improving diagnostic accuracy and treatment efficacy in spinal care.