SynthesisEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2020
Initial classification of low back and leg pain based on objective functional testing: a pilot study of machine learning applied to diagnostics.
Synthesis 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, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines 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
- Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.Neurosurgical review · 2022Pooled it
- The psychometric properties of the Dubousset Functional Test in patients with thoracic hyperkyphosis.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 · 2026Article
- Artificial Intelligence and Its Impact on the Management of Lumbar Degenerative Pathology: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Validity and accuracy of a machine learning predictive model in the exploitation of patient-related outcomes in spine surgery.Frontiers in surgery · 2025Article
- Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.Journal of personalized medicine · 2024Review
- Imaging of Spondylodiscitis: A Comprehensive Updated Review-Multimodality Imaging Findings, Differential Diagnosis, and Specific Microorganisms Detection.Microorganisms · 2024Review
- Applications of Artificial Intelligence in Pain Medicine.Current pain and headache reports · 2024Review
- Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning.Journal of clinical medicine · 2023Review
- 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
- An Evolution Gaining Momentum-The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases.Diagnostics (Basel, Switzerland) · 2022Review
- Five-Repetition Sit-to-Stand Test Performance in Healthy Individuals: Reference Values and Predictors From 2 Prospective Cohorts.Neurospine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment and thus provided an adjunctive dimension in patient assessment. The clinical interpretability and confounders of the 5R-STS remain poorly understood. In clinical use, it became apparent that 5R-STS performance may differ between patients with lumbar disk herniation (LDH), lumbar spinal stenosis (LSS) with or without low-grade spondylolisthesis, and chronic low back pain (CLBP). We seek to evaluate the extent of diagnostic information contained within 5R-STS testing.
methodsPatients were classified into gold standard diagnostic categories based on history, physical examination, and imaging. Crude and adjusted comparisons of 5R-STS performance were carried out among the three diagnostic categories. Subsequently, a machine learning algorithm was trained to classify patients into the three categories using only 5R-STS test time and patient age, gender, height, and weight.
resultsFrom two prospective studies, 262 patients were included. Significant differences in crude and adjusted test times were observed among the three diagnostic categories. At internal validation, classification accuracy was 96.2% (95% CI 87.099.5%). Classification sensitivity was 95.7%, 100%, and 100% for LDH, LSS, and CLBP, respectively. Similarly, classification specificity was 100%, 95.7%, and 100% for the three diagnostic categories.
conclusion5R-STS performance differs according to the etiology of back and leg pain, even after adjustment for demographic covariates. In combination with machine learning algorithms, OFI can be used to infer the etiology of spinal back and leg pain with accuracy comparable to other diagnostic tests used in clinical examination. These slides can be retrieved under Electronic Supplementary Material.
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