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.
Purpose: Cluster headache (CH) is characterized by circadian rhythmicity of the attacks, and it is known to respond exceptionally well to oxygen therapy. Furthermore, obstructive sleep apnea (OSA) frequently co-occurs with CH, and both conditions may be parallel outcomes of hypothalamic dysfunction rather than being causally related. The aim of this study was to analyze the association between CH characteristics and polysomnographic factors stratified by the severity of OSA in patients diagnosed with CH and OSA.
Methods We retrospectively analyzed the data of OSA patients with CH who were enrolled in the Korean Cluster Headache Registry and underwent polysomnography due to clinical suspicion of OSA. Basic demographic data, headache-related parameters, and polysomnographic parameters were analyzed according to the severity of OSA (apnea-hypopnea index: <15 or ≥15 per hour).
Results Twelve CH patients with OSA were evaluated. The onset age of CH was higher (38.5 years vs. 19.0 years, p=0.010), and the maximal duration of cluster bouts was longer (156.5 days vs. 47.0 days, p=0.037) in the moderate-to-severe OSA group than in the mild OSA group. Unlike other polysomnographic parameters, the apnea-hypopnea index and respiratory arousal index during rapid eye movement (REM) sleep were comparable across different OSA severity levels.
Conclusion The onset age and duration of cluster bouts were associated with the severity of OSA in CH patients. Additionally, the relatively high susceptibility to hypoxia during REM sleep in patients with mild OSA implies that interventions may be potentially advantageous, even in CH patients with mild OSA.
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