ArticleBMC musculoskeletal disorders2026
Prediction of lower limb compensation for adult degenerative spine patients using spinopelvic parameters: a machine learning approach.
Article in BMC musculoskeletal disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
STUDY
designRetrospective study.
objectiveThis work aims to estimate using machine learning the occurrence of knee flexion in relation to spinopelvic parameters in lumbar degenerative pathologies.
backgroundAdult spinal deformity (ASD) significantly impairs quality of life, prompting the need for preoperative alignment assessment. Common compensatory mechanisms include thoracic hypokyphosis and pelvic retroversion. With the advent of full-body EOS imaging, spinopelvic measures understanding improved. In fact, there are strong correlations between knee flexion and HRQoL underscore the importance of estimating knee flexion for informing interventions to improve well-being in patients.
methodsThis retrospective single-center study includes patients with degenerative spinal pathologies necessitating surgical fusion. Parameters were extracted from radiographs using SterEOS
resultsThe study comprised 201 subjects (104 men, 97 women; mean age: 63.57 years [25-89]) with various spinal pathologies, primarily disc disease and spinal stenosis. Quantitative methods were assessed with partial least square (PLS) regression showing initial promise but declining predictive ability with additional components. For classification methods, the support vector machine (SVM) demonstrated good performance on test data, with high accuracy (0.68) and sensitivity on test data.
conclusionThis study suggests that spinopelvic parameters alone may be used to estimate lower limb compensation. Supervised machine learning classification, with 86% sensitivity, shows promise for estimating knee flexion. Further validation on larger, more homogeneous databases is needed.
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