Application of machine learning in migraine classification: a call for study design standardization and global collaboration.
Migraine is a complex neurological disorder with diverse clinical phenotypes and a multifaceted pathophysiology, which poses substantial challenges for accurate diagnosis, subtype differentiation, and biomarker discovery. Machine learning (ML) techniques have emerged as promising tools for classifyi...
| Publicado en: | Journal of Headache & Pain Vol. 26; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
10/2/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188453271&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188453271 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11292369 O3T jtl: Journal of Headache & Pain issn: 11292369 maglogo: N pubinfo: dt: 10/2/2025 vid: 26 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188453271 188453271 188453271 10.1186/s10194-025-02134-9 188453271 ppf: 1 ppct: 13 formats: tig: atl: Application of machine learning in migraine classification: a call for study design standardization and global collaboration. aug: au: Petrušić, Igor Messina, Roberta Pellesi, Lanfranco Azorin, David Garcia Chiang, Chia-Chun Pietra, Adriana Della Ha, Woo-Seok Labastida-Ramirez, Alejandro Onan, Dilara Ornello, Raffaele Raffaelli, Bianca Rubio-Beltran, Eloisa Ruscheweyh, Ruth Tana, Claudio Vuralli, Doga Waliszewska-Prosół, Marta Wang, Wei Wells-Gatnik, William David Martelletti, Paolo Raggi, Alberto affil: https://ror.org/02qsmb048 Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, 12-16 Studentski Trg Street, 11000, Belgrade, Serbia sug: subj: Machine Learning Utilization Migraine Classification Pain Management Methods World Health Collaboration Migraine Diagnosis Study Design Methods Human Funding Source Systematic Review PubMed Classification Algorithms Artificial Intelligence Ethical Issues Individualized Medicine Discriminant Analysis Research, Medical Biological Markers Deep Learning Methods Neuroradiography Descriptive Statistics Phenotype International Relations ab: Migraine is a complex neurological disorder with diverse clinical phenotypes and a multifaceted pathophysiology, which poses substantial challenges for accurate diagnosis, subtype differentiation, and biomarker discovery. Machine learning (ML) techniques have emerged as promising tools for classifying migraine patients and uncovering the underlying neurobiological mechanisms that differentiate migraine types and subtypes. This systematic review identifies current ML classification models for migraine types and subtypes, evaluating the quality, reproducibility, and clinical utility of published studies. The findings demonstrate that current ML models, particularly support vector machines and linear discriminant analysis, can accurately classify migraine patients based on structural and functional neuroimaging features with accuracies ranging from 75 to 98%. However, quality assessment revealed significant methodological heterogeneity across studies, including inconsistent reporting of model performance, insufficient patient phenotyping, small and imbalanced datasets, and limited external validation. These limitations hinder the global generalizability and reproducibility of these studies. We propose a roadmap for future research emphasizing well-characterized clinical subgrouping, standardized data acquisition and feature engineering protocols, transparency in model development and reporting, and collaborative multicentric designs to enable large-scale validation. Furthermore, this review stresses the importance of incorporating real-world phenotypic data, such as treatment response, comorbidities, and digital phenotyping metrics, to enrich ML models and support the transition toward precision medicine in migraine care. Ultimately, this review highlights the urgent need for methodological rigor in migraine ML classification studies to bridge the gap between experimental success and clinical applicability. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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