Data and knowledge-driven imaging biomarkers for lumbar aging and degenerative risk stratification monitoring

Lumbar intervertebral disc degeneration, a key indicator of aging in the human movement system, is linked to increasing global cases of low back pain. Current diagnostic methods rely on imaging and physician experience, lacking predictive tools and personalized treatment strategies. This study used a multicenter lumbar MRI dataset to map disc degeneration in the Chinese population, revealing three accelerated degeneration phases during the lifecycle. Age heatmaps highlighted the degeneration rate of L1-L3 segments, highly synchronized with true age, serving as a baseline for physiological aging estimation. A contrastive learning-based slice ensemble network achieved a mean absolute error of 2.59 years in age estimation, and multi-center validation confirmed its reliability. Two digital imaging biomarkers, Age Delta and Age Selta, were proposed and preliminarily validated in longitudinal cases as a proof-of-concept demonstration. This study primarily demonstrates the feasibility and potential clinical value of data-driven lumbar aging biomarkers.