ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2020
Interpretable machine learning models for classifying low back pain status using functional physiological variables.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Machine learning-based prediction of disability in subacute low back pain: a primary care study on clinical and psychosocial determinants.BMC medical informatics and decision making · 2026Observational
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.Journal of personalized medicine · 2024Review
- Artificial intelligence: a new cutting-edge tool in spine surgery.Asian spine journal · 2024Article
- The Application of Surface Electromyography Technology in Evaluating Paraspinal Muscle Function.Diagnostics (Basel, Switzerland) · 2024Review
- Applications of Artificial Intelligence in Pain Medicine.Current pain and headache reports · 2024Review
- Comparative analysis of machine learning models for efficient low back pain prediction using demographic and lifestyle factors.Journal of back and musculoskeletal rehabilitation · 2024Article
- Binary classification of non-specific low back pain condition based on the combination of B-mode ultrasound and shear wave elastography at multiple sites.Frontiers in physiology · 2023Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Artificial intelligence in spine care: current applications and future utility.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
- Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test.Sensors (Basel, Switzerland) · 2022Article
- Interpretable machine learning models for classifying low back pain status using functional physiological variables.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeTo evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting.
methodsMotion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3.
resultsSeven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text] = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] = 0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] = 0.16) in model 3.
conclusionThe ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material.
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