An interpretable machine learning model for MRI-based morphological classification of lumbar disc herniation using functional biomechanical features.
- 1School of Economics and Management, Shanghai University of Political Science and Law, Shanghai, China.
- 2Department of Traumatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Abstract
Introduction
Lumbar disc herniation (LDH) is commonly assessed using magnetic resonance imaging (MRI), although disc morphology does not necessarily correspond to pain intensity, functional impairment, neurological findings, or prognosis. This retrospective single-center exploratory study examined whether functional biomechanical indicators were associated with MRI-based morphological classification of LDH.Methods
A total of 58 patients were included, comprising 15 patients with disc bulging and 43 with disc protrusion or extrusion. Five predictors-lumbar extension, straight-leg-raising asymmetry, side-flexion asymmetry, gluteus medius asymmetry, and right Jiaji regional surface electromyography root mean square-were used to construct an interpretable L2-regularized logistic regression model. Model performance was evaluated using repeated stratified five-fold cross-validation repeated 10 times, with participant-level out-of-fold probabilities averaged across repetitions. Predictors were selected before cross-validation; therefore, the reported performance may include selection-induced optimism.Results
The model achieved a receiver operating characteristic area under the curve of 0.705 (95% confidence interval: 0.537-0.851), a precision-recall area under the curve of 0.844, and a Brier score of 0.218. At the exploratory Youden threshold of 0.566, balanced accuracy was 0.714, sensitivity was 0.628, specificity was 0.800, positive predictive value was 0.900, and negative predictive value was 0.429. A prespecified threshold of 0.500 yielded a balanced accuracy of 0.692, sensitivity of 0.651, and specificity of 0.733. Calibration was suboptimal, with an intercept of 0.999 and a slope of 0.752. In a separate exploratory bootstrap analysis, coefficient sign stability ranged from 97.0% to 99.9%, although the intervals remained wide.Discussion
These findings suggest preliminary associations between selected functional biomechanical indicators and MRI-based disc morphology. Given the small and imbalanced sample, predictor-selection optimism, limited negative predictive performance, suboptimal calibration, and absence of external validation, the model should be regarded as hypothesis-generating rather than as a standalone clinical decision-support tool.
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BibTeX
@article{yuan2026interpretable,
title = {{An interpretable machine learning model for MRI-based morphological classification of lumbar disc herniation using functional biomechanical features.}},
author = {Guanghui Yuan and Can Cui and Yong Xu and Bin Xue},
journal = {Frontiers in bioengineering and biotechnology},
year = {2026},
month = oct,
doi = {10.3389/fbioe.2026.1856872},
url = {https://europepmc.org/article/MED/42898409},
note = {CC BY},
}
RIS
TY - JOUR
TI - An interpretable machine learning model for MRI-based morphological classification of lumbar disc herniation using functional biomechanical features.
AU - Guanghui Yuan
AU - Can Cui
AU - Yong Xu
AU - Bin Xue
PY - 2026
DA - 2026/10/05
JO - Frontiers in bioengineering and biotechnology
DO - 10.3389/fbioe.2026.1856872
UR - https://europepmc.org/article/MED/42898409
AB - Introduction Lumbar disc herniation (LDH) is commonly assessed using magnetic resonance imaging (MRI), although disc morphology does not necessarily correspond to pain intensity, functional impairment, neurological findings, or prognosis. This retrospective single-center exploratory study examined whether functional biomechanical indicators were associated with MRI-based morphological classification of LDH.Methods A total of 58 patients were included, comprising 15 patients with disc bulging and 43 with disc protrusion or extrusion. Five predictors-lumbar extension, straight-leg-raising asymmetry, side-flexion asymmetry, gluteus medius asymmetry, and right Jiaji regional surface electromyography root mean square-were used to construct an interpretable L2-regularized logistic regression model. Model performance was evaluated using repeated stratified five-fold cross-validation repeated 10 times, with participant-level out-of-fold probabilities averaged across repetitions. Predictors were selected before cross-validation; therefore, the reported performance may include selection-induced optimism.Results The model achieved a receiver operating characteristic area under the curve of 0.705 (95% confidence interval: 0.537-0.851), a precision-recall area under the curve of 0.844, and a Brier score of 0.218. At the exploratory Youden threshold of 0.566, balanced accuracy was 0.714, sensitivity was 0.628, specificity was 0.800, positive predictive value was 0.900, and negative predictive value was 0.429. A prespecified threshold of 0.500 yielded a balanced accuracy of 0.692, sensitivity of 0.651, and specificity of 0.733. Calibration was suboptimal, with an intercept of 0.999 and a slope of 0.752. In a separate exploratory bootstrap analysis, coefficient sign stability ranged from 97.0% to 99.9%, although the intervals remained wide.Discussion These findings suggest preliminary associations between selected functional biomechanical indicators and MRI-based disc morphology. Given the small and imbalanced sample, predictor-selection optimism, limited negative predictive performance, suboptimal calibration, and absence of external validation, the model should be regarded as hypothesis-generating rather than as a standalone clinical decision-support tool.
SN - 2296-4185
ER -