ReviewCurrent rheumatology reports2017
Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.
Review in Current rheumatology reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05860205 (A Feasibility and Pilot Study of a Multiple Component Mobile-aid Pain Reduction Intervention to Decrease Myofascial Pain Syndrome), which is not on this map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Feasibility and Pilot Study of a Multiple Component Mobile-aid Pain Reduction Intervention to Decrease Myofascial Pain Syndrome
Who cites it
25 citing papers in PubMed.
- Objective Measurement of Musculoskeletal Pain: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Recent developments and future avenues for human corticospinal neuroimaging.Frontiers in human neuroscience · 2024Review
- Machine learning-based evaluation of spontaneous pain and analgesics from cellular calcium signals in the mouse primary somatosensory cortex using explainable features.Frontiers in molecular neuroscience · 2024Article
- Chronic neuropathic pain components in whiplash-associated disorders correlate with metabolite concentrations in the anterior cingulate and dorsolateral prefrontal cortex: a consensus-driven MRS re-examination.Frontiers in medicine · 2024Article
- Mapping the Design Space of Technology-Based Solutions for Better Chronic Pain Care: Introducing the Pain Tech Landscape.Psychosomatic medicine · 2023Review
- Circuits and Biomarkers of the Central Nervous System Relating to Astronaut Performance: Summary Report for a NASA-Sponsored Technical Interchange Meeting.Life (Basel, Switzerland) · 2023Article
- Altered habenular connectivity in chronic low back pain: An fMRI and machine learning study.Human brain mapping · 2023Article
- The reliability of two prospective cortical biomarkers for pain: EEG peak alpha frequency and TMS corticomotor excitability.Journal of neuroscience methods · 2023Article
- Classification of primary dysmenorrhea by brain effective connectivity of the amygdala: a machine learning study.Brain imaging and behavior · 2022Article
- Value assessment of artificial intelligence in medical imaging: a scoping review.BMC medical imaging · 2022Article
- Neuroimaging Assessment of Pain.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Review
- A classification algorithm to predict chronic pain using both regression and machine learning - A stepwise approach.Applied nursing research : ANR · 2021Article
- A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs.Scientific reports · 2021Article
- Artificial intelligence for good health: a scoping review of the ethics literature.BMC medical ethics · 2021Article
- Brain Imaging Biomarkers for Chronic Pain.Frontiers in neurology · 2021Review
- Characterizing Brain Tumor Regions Using Texture Analysis in Magnetic Resonance Imaging.Frontiers in neuroscience · 2021Article
- Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm.Frontiers in public health · 2021Article
- Grand Challenges in Neuromodulatory Interventions.Frontiers in pain research (Lausanne, Switzerland) · 2021Article
- An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study.Pain and therapy · 2020Article
- Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.