Introduction

Musculoskeletal pain—encompassing discomfort in joints, muscles, ligaments, tendons, and bones—is a highly prevalent and multifaceted health challenge worldwide. In the United States alone, it affects nearly 21% of adults, with chronic cases contributing to persistent pain, disability, reduced productivity, and escalating healthcare costs. Globally, the burden is immense: in 2021, there were an estimated 1.686 billion cases, nearly double the prevalence recorded in 1990, exerting substantial strain on health systems and economies.

Accurate classification of musculoskeletal pain is critical for guiding diagnosis and tailoring treatment—ranging from conservative management to surgical intervention and rehabilitation. Yet, achieving diagnostic precision remains difficult due to the condition’s multifactorial nature, overlapping symptoms, and heterogeneous clinician interpretations. The recent International Classification of Diseases (ICD-11) update, which differentiates chronic primary from secondary musculoskeletal pain, reflects a growing need for more nuanced diagnostic frameworks that incorporate underlying pathophysiology.

In this context, machine learning (ML) is emerging as a transformative tool capable of introducing objectivity, precision, and efficiency into pain classification. By integrating diverse datasets—from demographic profiles and biomechanical measures to neuroimaging signals—ML models have achieved accuracies often exceeding 90% in identifying pain intensity, risk factors, and typologies. These approaches address the limitations of subjective symptom reporting and complex etiologies, offering clinicians a powerful means to refine disease taxonomy, improve diagnostic accuracy, and enhance patient outcomes.1,2,3,4

Machine Learning: A New Diagnostic Partner

Machine learning refers to computational methods that learn from data to support and improve decision-making. In healthcare, this typically involves supervised learning—training models on labeled datasets to predict defined outcomes—and unsupervised learning, which uncovers hidden patterns without pre-labeled categories.

For musculoskeletal pain classification, ML excels in analyzing large, multimodal datasets that combine clinical history, imaging data (e.g., MRI), laboratory biomarkers, wearable sensor outputs, and patient-reported outcomes. This integration enables the detection of subtle, nonlinear associations beyond the capacity of human observation, allowing for more granular pain categorization and personalized prognostication.

Recent studies demonstrate that ML-generated prognostic profiles, which incorporate multiple patient and disease dimensions, enhance clinical decision support and predict treatment response with high accuracy. Neural networks optimized with particle swarm algorithms, for example, have effectively classified pain risk based on demographic, lifestyle, and physiological inputs. Similarly, combining wearable device metrics with EEG-based biomarkers has advanced the objective measurement of pain intensity. Collectively, these innovations position ML as a sophisticated diagnostic partner that can capture the complex, multimodal nature of musculoskeletal pain and guide earlier, more individualized interventions.5,6

Data Sources Driving ML-Based Classification

The strength of ML in musculoskeletal pain classification lies in its capacity to integrate heterogeneous data sources into unified predictive models.

Imaging data—from MRI, ultrasound, and X-ray—allow advanced algorithms to extract subtle structural features linked to pain pathology. Biomechanical data from gait analysis, posture tracking, and joint kinematics provide functional insights into movement-related impairments. Clinical records contribute demographic, comorbidity, and electronic health record (EHR) information, situating pain within a broader health context. Physiological signals, such as electromyography (EMG) and nerve conduction studies, offer objective measures of neuromuscular function and pain processing pathways. Finally, patient-reported outcomes—gathered through digital surveys, pain diaries, and mobile health platforms—capture subjective experiences essential for personalized care.

Integrating these diverse modalities into multimodal ML models produces richer, more accurate pain phenotypes. Such models enhance diagnostic precision, improve prognostic accuracy, and facilitate targeted, individualized treatment strategies. This comprehensive, data-driven approach addresses the limitations of relying on single data types and aligns with the biopsychosocial framework that underpins contemporary pain management.7,8,9,10

Reference:

  1. Zhuang J, Mei H, Fang F, Ma X. What Is New in Classification, Diagnosis and Management of Chronic Musculoskeletal Pain: A Narrative Review. Front Pain Res (Lausanne). 2022 Jul 15;3:937004. doi: 10.3389/fpain.2022.937004. PMID: 35910265; PMCID: PMC9335004.

  2. Fouad DM, Mahfouz MM, Mohamed MM, Elzanaty MY, Abd El-Hafeez T. Classification of musculoskeletal pain using machine learning. Sci Rep. 2025 Jul 25;15(1):27158. doi: 10.1038/s41598-025-12049-9. PMID: 40715236; PMCID: PMC12297230.

  3. Matsangidou, M., Liampas, A., Pittara, M. et al. Machine Learning in Pain Medicine: An Up-To-Date Systematic Review. Pain Ther 10, 1067–1084 (2021). https://doi.org/10.1007/s40122-021-00324-2

  4. Liu M, Rong J, An X, Li Y, Min Y, Yuan G, Yang Y and Li M (2025) Global, regional, and national burden of musculoskeletal disorders, 1990–2021: an analysis of the global burden of disease study 2021 and forecast to 2035. Front. Public Health. 13:1562701. doi: 10.3389/fpubh.2025.1562701

  5. Imai H, Kanie Y, Yoshimoto S, Yamamoto N, Furuya M, Fujimori T, Okada S. Classification accuracy of pain intensity induced by leg blood flow restriction during walking using machine learning based on electroencephalography. Sci Rep. 2025 Jul 31;15(1):27955. doi: 10.1038/s41598-025-13433-1. PMID: 40745441; PMCID: PMC12314105.

  6. Zmudzki F and Smeets RJEM (2023) Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment. Front. Pain Res.4:1177070. doi: 10.3389/fpain.2023.1177070

  7. Rosenberg N. Objective Measurement of Musculoskeletal Pain: A Comprehensive Review. Diagnostics (Basel). 2025 Jun 22;15(13):1581. doi: 10.3390/diagnostics15131581. PMID: 40647580; PMCID: PMC12249086.

  8. Westbury LD, Fuggle NR, Pereira D, Oka H, Yoshimura N, Oe N, Mahmoodi S, Niranjan M, Dennison EM, Cooper C. Machine learning as an adjunct to expert observation in classification of radiographic knee osteoarthritis: findings from the Hertfordshire Cohort Study. Aging Clin Exp Res. 2023 Jul;35(7):1449-1457. doi: 10.1007/s40520-023-02428-5. Epub 2023 May 19. PMID: 37202598; PMCID: PMC10284967.

  9. Cascella M, Schiavo D, Cuomo A, Ottaiano A, Perri F, Patrone R, Migliarelli S, Bignami EG, Vittori A, Cutugno F. Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives. Pain Res Manag. 2023 Jun 28;2023:6018736. doi: 10.1155/2023/6018736. PMID: 37416623; PMCID: PMC10322534.

  10. Zeng X, Tang W, Yang J, Lin X, Du M, Chen X, Yuan Z, Zhang Z, Chen Z. Diagnosis of Chronic Musculoskeletal Pain by Using Functional Near-Infrared Spectroscopy and Machine Learning. Bioengineering (Basel). 2023 Jun 1;10(6):669. doi: 10.3390/bioengineering10060669. PMID: 37370599; PMCID: PMC10294811.