ArticleThe journal of headache and pain2023
Machine learning approach for Migraine Aura Complexity Score prediction based on magnetic resonance imaging data.
Article in The journal of headache and pain, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of machine learning in migraine classification: a call for study design standardization and global collaboration.The journal of headache and pain · 2025Pooled it
- Artificial intelligence in headache medicine: between automation and the doctor-patient relationship. A systematic review.The journal of headache and pain · 2025Pooled it
- Functional Magnetic Resonance Imaging for Investigating the Role of the Hippocampus in Migraine with Aura.Diagnostics (Basel, Switzerland) · 2026Article
- Triggers, prodrome, aura, and headache interactions in migraine with typical aura: a prospective deep-phenotyping study.The journal of headache and pain · 2026Observational
- Clinical Prediction of Posttreatment Migraine Recurrence Using Biofeedback Data: A Machine Learning Framework for Enhanced Patient Stratification and Treatment Monitoring.BioMed research international · 2026Article
- Machine learning-derived diagnostic model of epithelial ovarian cancer based on gut microbiome signatures.Journal of translational medicine · 2025Article
- Hallmarks of primary headache: part 1 - migraine.The journal of headache and pain · 2024Review
- Subsegmentation of the hippocampus in subgroups of migraine with aura patients: advanced structural neuroimaging study.The journal of headache and pain · 2024Article
- Application of Artificial Intelligence in the Headache Field.Current pain and headache reports · 2024Review
- Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.The journal of headache and pain · 2024Review
- The Ethical Stewardship of Artificial Intelligence in Chronic Pain and Headache: A Narrative Review.Current pain and headache reports · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrevious studies have developed the Migraine Aura Complexity Score (MACS) system. MACS shows great potential in studying the complexity of migraine with aura (MwA) pathophysiology especially when implemented in neuroimaging studies. The use of sophisticated machine learning (ML) algorithms, together with deep profiling of MwA, could bring new knowledge in this field. We aimed to test several ML algorithms to study the potential of structural cortical features for predicting the MACS and therefore gain a better insight into MwA pathophysiology.
methodsThe data set used in this research consists of 340 MRI features collected from 40 MwA patients. Average MACS score was obtained for each subject. Feature selection for ML models was performed using several approaches, including a correlation test and a wrapper feature selection methodology. Regression was performed with the Support Vector Machine (SVM), Linear Regression, and Radial Basis Function network.
resultsSVM achieved a 0.89 coefficient of determination score with a wrapper feature selection. The results suggest a set of cortical features, located mostly in the parietal and temporal lobes, that show changes in MwA patients depending on aura complexity.
conclusionsThe SVM algorithm demonstrated the best potential in average MACS prediction when using a wrapper feature selection methodology. The proposed method achieved promising results in determining MwA complexity, which can provide a basis for future MwA studies and the development of MwA diagnosis and treatment.
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