Headache and Pain Research

Original Article

Natural Diagnostic Classes of Headache Disorders: Latent Class Analysis of a Population-Based Study: Wonwoo Lee, Seok-Jae Heo, Jungyon Yum, Min Kyung Chu; Headache Pain Res. 2026;27(1):30-42. Published online February 26, 2026; DOI: https://doi.org/10.62087/hpr.2026.0004

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Abstract PDF Supplementary Material: Purpose: The International Classification of Headache Disorders, 3rd edition (ICHD-3), defines headache diagnoses based on combinations of clinical symptoms. Diagnostic overlap is common, and symptom variability complicates diagnostic classification. We evaluated natural classes of headache disorders using a statistical approach and compared these classes with ICHD-3 diagnostic categories.
Methods
Data from a nationwide, population-based web survey on headache and sleep conducted in South Korea (n=3,030) were analyzed. Participants who reported headache within the past year (n=1,938) were included. Latent class analysis was performed using categorical ICHD-3 diagnostic criteria to identify distinct classes. The characteristics of each class and the distribution of ICHD-3 primary headache diagnoses were examined.
Results
Nine classes were identified, comprising 626, 54, 248, 148, 187, 143, 79, 61, and 392 individuals. Three classes were tension-type headache (TTH)–like: Class 1 was male-dominant mild bilateral TTH, Class 8 represented classic, severe TTH, and Class 9 was mild unilateral TTH. Class 4 showed a typical migraine phenotype and contained most migraine cases. Classes 5 and 6 were dominated by probable migraine (PM) and differed mainly in sensory sensitivity and disability, which were higher in Class 6. Classes 2, 3, and 7 were categorized as “other headache.” Class 2 had the highest prevalence of medication-overuse headache (MOH), whereas Class 3 was characterized by mild headache with nausea. Class 7 showed a mixed-type profile with prominent photophobia. Severity and central sensitization markers were key classifiers.
Conclusion
Latent class analysis identified nine clinically distinct headache classes. PM was clearly distinct from both TTH and migraine. One subtype within the “other headache” class showed the highest MOH burden.

Review Article

The Current Role of Artificial Intelligence in the Field of Headache Disorders, with a Focus on Migraine: A Systemic Review: Wonwoo Lee, Min Kyung Chu; Headache Pain Res. 2025;26(1):48-65. Published online February 17, 2025; DOI: https://doi.org/10.62087/hpr.2024.0024

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5 Citations

The application of artificial intelligence (AI) in the field of headache disorders, particularly migraine, is rapidly expanding, and AI has demonstrated significant potential for diagnosis, treatment, and research. This review examines the current role of AI in migraine management, categorizing AI applications into diagnosis and classification, assessment of treatment response, prediction of migraine attacks, and research. A systematic search of literature published between 2000 and 2024 was conducted, following PRISMA guidelines and utilizing the snowball technique. Of the 398 articles identified, along with five additional articles, 61 were finally reviewed. The results highlight promising AI applications, including the use of patient questionnaires, natural language processing, and imaging for migraine diagnosis, as well as predicting treatment responses and forecasting migraine attacks. Nonetheless, challenges remain in improving the accuracy, generalizability, validation, and clinical relevance of AI applications. Despite the substantial promise of AI for improving migraine management, it does not always guarantee better results than traditional methods. Careful consideration of the study design and method selection is crucial. Additionally, the interpretation of AI-generated results, particularly those from generative models, requires caution to avoid potential pitfalls.

Citations

Citations to this article as recorded by

Primary Headache Disorders: The Virtual Neurologic Examination in Telehealth Practice
Lindsey Plato-Johnson, Caroline Stowe
The Journal for Nurse Practitioners.2026; 22(1): 105593. CrossRef
Predicting Pharmacological Treatment Response in Migraine Using AI/ML: A Scoping Review of the Evidence and Future Directions
Martina Giacon, Salvatore Terrazzino
Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy.2026;[Epub] CrossRef
Injection-Based Therapies for Migraine in Older Adults: A Narrative Review of OnabotulinumtoxinA, Greater Occipital Nerve Block, and Anti Calcitonin Gene-Related Peptide Monoclonal Antibodies
Mi-Kyoung Kang, Soohyun Cho, Byung-Kun Kim, Heui-Soo Moon, Mi Ji Lee, Soo-Kyoung Kim, Hong-Kyun Park, Min-Kyung Chu, Woo-Seok Ha, Byung-Su Kim, Soo-Jin Cho
Journal of Korean Medical Science.2025;[Epub] CrossRef
Validation of an AI-Based platform for structured diagnosis of headache disorders using ICHD-3 criteria
João Brainer Clares de Andrade, Thiago Bulhões da Silva Costa, Júlia Lima Vasconcelos, Thiago Luís Marques Lopes, Mateus Dutra Balsells, Vinícius Luiz Cristofolini, Sophia Oliveira Querobin, Flavio Moura Rezende Filho
BMC Neurology.2025;[Epub] CrossRef
MOBILE HEALTH AND ARTIFICIAL INTELLIGENCE SOLUTIONS FOR MIGRAINE MANAGEMENT- A LITERATURE REVIEW
Kinga Szyszka, Anna Baranowska, Marta Cieślak, Laura Kurczoba, Aleksandra Oparcik, Anastazja Orłowa, Anita Pakuła, Klaudia Martyna Patrzykąt, Julia Pawłowska, Kamil Turlej
International Journal of Innovative Technologies in Social Science.2025;[Epub] CrossRef

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