Prediction of lower limb compensation for adult degenerative spine patients using spinopelvic parameters: a machine learning approach.

STUDY

DESIGN

Retrospective study.

OBJECTIVE

This work aims to estimate using machine learning the occurrence of knee flexion in relation to spinopelvic parameters in lumbar degenerative pathologies.

BACKGROUND

Adult 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.

METHODS

This retrospective single-center study includes patients with degenerative spinal pathologies necessitating surgical fusion. Parameters were extracted from radiographs using SterEOS® software and included spinopelvic parameters. Knee-related parameters were not used. The database was split into learning (80%) and test (20%) sets. Parameters' representativeness is evaluated. Predictive models, included quantitative and categorical methods.

RESULTS

The 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.

CONCLUSION

This 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.