ArticleFrontiers in surgery2022
Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning prediction of surgical approach and clinical heterogeneity in cervical spondylotic myelopathy.BMC musculoskeletal disorders · 2026Article
- Artificial intelligence and machine learning based predictive analytics in cervical myelopathy - a scoping review.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
- Article
- Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms.Biomolecules & biomedicine · 2024Article
- Motor Pathways Reorganization following Surgical Decompression for Degenerative Cervical Myelopathy: A Combined Navigated Transcranial Magnetic Stimulation and Clinical Outcome Study.Brain sciences · 2024Article
- Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity.Scientific reports · 2024Article
- Classification and prognostic factors of patients with cervical spondylotic myelopathy after surgical treatment: a cluster analysis.Scientific reports · 2024Article
- Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery.Infection and drug resistance · 2023Article
- Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning.Annals of medicine · 2023Article
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Authors and funding
15 authors.
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Abstract
Background: Anterior cervical decompression and fusion can effectively treat cervical spondylotic myelopathy (CSM). Accurately classifying patients with CSM who have undergone anterior cervical decompression and fusion is the premise of precision medicine. In this study, we used machine learning algorithms to classify patients and compare the postoperative efficacy of each classification. Methods: A total of 616 patients with cervical spondylotic myelopathy who underwent anterior cervical decompression and fusion were enrolled. Unsupervised machine learning algorithms (UMLAs) were used to cluster subjects according to similar clinical characteristics. Then, the results of clustering were visualized. The surgical outcomes were used to verify the accuracy of machine learning clustering. Results: We identified two clusters in these patients who had significantly different baseline clinical characteristics, preoperative complications, the severity of neurological symptoms, and the range of decompression required for surgery. UMLA divided the CSM patients into two clusters according to the severity of their illness. The repose to surgical treatment between the clusters was significantly different. Conclusions: Our results showed that UMLA could be used to rationally classify a heterogeneous cohort of CSM patients effectively, and thus, it might be used as the basis for a data-driven platform for identifying the cluster of patients who can respond to a particular treatment method.
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