ArticleCureus2023
Developing an Artificial Intelligence-Based Pediatric and Adolescent Migraine Diagnostic Model.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in headache medicine: between automation and the doctor-patient relationship. A systematic review.The journal of headache and pain · 2025Pooled it
- [Development and validation of Cefalytics: an artificial intelligence-based application for the rapid diagnosis of primary headaches].Atencion primaria · 2026Article
- Addressing the Treatment Gap for Pediatric Headache in Underserved Regions.Current pain and headache reports · 2026Review
- [CefaleApp: A Mobile Application for Migraine Diagnosis in Primary Health Care].Revista de neurologia · 2026Article
- Applying artificial intelligence in neurodevelopmental disorders management and research.European journal of medical research · 2026Review
- Artificial intelligence-assisted neuropharmacology: reshaping drug discovery and translational strategies for neurological diseases.Turkish journal of medical sciences · 2026Review
- Research status, hotspots and perspectives of artificial intelligence applied to pain management: a bibliometric and visual analysis.Updates in surgery · 2025Article
- Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management.Current pain and headache reports · 2024Review
- The Clinical Relevance of Artificial Intelligence in Migraine.Brain sciences · 2024Review
- Treatment Patterns for and Characteristics of Headache in Children and Adolescents Aged 6-17 Years in Japan: A Retrospective Cross-Sectional and Longitudinal Analysis of Health Insurance Claims Data.Life (Basel, Switzerland) · 2024Article
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12 authors.
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Abstract
Introduction Misdiagnosis of pediatric and adolescent migraine is a significant problem. The first artificial intelligence (AI)-based pediatric migraine diagnosis model was made utilizing a database of questionnaires obtained from a previous epidemiological study, the Itoigawa Benizuwaigani Study. Methods The AI-based headache diagnosis model was created based on the internal validation based on a retrospective investigation of 909 patients (636 training dataset for model development and 273 test dataset for internal validation) aged six to 17 years diagnosed based on the International Classification of Headache Disorders 3rd edition. The diagnostic performance of the AI model was evaluated. Results The dataset included 234/909 (25.7%) pediatric or adolescent patients with migraine. The mean age was 11.3 (standard deviation 3.17) years. The model's accuracy, sensitivity (recall), specificity, precision, and F-values for the test dataset were 94.5%, 88.7%, 96.5%, 90.0%, and 89.4%, respectively. Conclusions The AI model exhibited high diagnostic performance for pediatric and adolescent migraine. It holds great potential as a powerful tool for diagnosing these conditions, especially when secondary headaches are ruled out. Nonetheless, further data collection and external validation are necessary to enhance the model's performance and ensure its applicability in real-world settings.
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