Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.

Artificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorithms to analyze extensive health and medical data more efficiently. In headache disorders, particularly migraine, AI has shown promising potential in various applications, such as understanding disease mechanisms and predicting patient responses to therapies. Implementing next-generation AI in headache research and treatment could transform the field by providing precision treatments and augmenting clinical practice, thereby improving patient and public health outcomes and reducing clinician workload. AI-powered tools, such as large language models, could facilitate automated clinical notes and faster identification of effective drug combinations in headache patients, reducing cognitive burdens and physician burnout. AI diagnostic models also could enhance diagnostic accuracy for non-headache specialists, making headache management more accessible in general medical practice. Furthermore, virtual health assistants, digital applications, and wearable devices are pivotal in migraine management, enabling symptom tracking, trigger identification, and preventive measures. AI tools also could offer stress management and pain relief solutions to headache patients through digital applications. However, considerations such as technology literacy, compatibility, privacy, and regulatory standards must be adequately addressed. Overall, AI-driven advancements in headache management hold significant potential for enhancing patient care, clinical practice and research, which should encourage the headache community to adopt AI innovations.

Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features.

Background: Juvenile myoclonic epilepsy (JME) is a common adolescent epilepsy characterized by myoclonic, generalized tonic-clonic, and sometimes absence seizures. Prognosis varies, with many patients experiencing relapse despite pharmacological treatment. Recent advances in imaging and artificial intelligence suggest that combining microstructural brain changes with traditional clinical variables can enhance potential prognostic biomarkers identification. Methods: A retrospective study was conducted on patients with JME at the Severance Hospital, analyzing clinical variables and magnetic resonance imaging (MRI) data. Machine learning models were developed to predict prognosis using clinical and radiological features. Results: The study utilized six machine learning models, with the XGBoost model demonstrating the highest predictive accuracy (AUROC 0.700). Combining clinical and MRI data outperformed models using either type of data alone. The key features identified through a Shapley additive explanation analysis included the volumes of the left cerebellum white matter, right thalamus, and left globus pallidus. Conclusions: This study demonstrated that integrating clinical and radiological data enhances the predictive accuracy of JME prognosis. Combining these neuroanatomical features with clinical variables provided a robust prediction of JME prognosis, highlighting the importance of integrating multimodal data for accurate prognosis.

Trends in Triptan Usage in Korea: A Population-Based Cohort Study.

BACKGROUND: Migraine presents a significant global health problem that emphasizes the need for efficient acute treatment options. Triptans, introduced in the early 1990s, have substantially advanced migraine management owing to their effectiveness compared to that of traditional medications. However, data on triptan use in migraine management from Asian countries, where migraines tend to have milder symptoms than those in European and North American countries, are limited. This study aimed to identify the trends in triptan usage in Korea. METHODS: This retrospective cohort study used data from the Korean National Health Insurance Service-National Sample Cohort spanning from 2002 to 2019. Patients with migraine were identified using the International Classification of Diseases 10th revision codes, and triptan prescriptions were evaluated annually in terms of quantity, pills per patient, and associated costs. The distribution of triptan prescriptions across different medical specialties was also examined. Factors contributing to the odds of triptan use were analyzed using multivariable logistic regression. RESULTS: From 2002 to 2019, the total number of triptan tablets, prescriptions, and patients using triptans increased by 24.0, 17.1, and 13.6 times, respectively, with sumatriptan being the most frequently prescribed type of triptan. Additionally, the number of prescriptions and related costs have consistently increased despite stable pricing because of government regulation. By 2019, only approximately one-tenth of all patients with migraines had been prescribed triptans, although there was a notable increase in prescriptions over the study period. These prescription patterns varied according to the physician's specialty. After adjusting for patient-specific factors including age and sex, the odds of prescribing triptans were higher for neurologists than for internal medicine physicians (odds ratio 2.875, P < 0.001), while they were lower for general practitioners (odds ratio 0.220, P < 0.001). CONCLUSION: The findings revealed an increasing trend in triptan use among individuals with migraines in Korea, aligning with global usage patterns. Despite these increases, the overall prescription rate of triptans remains low, indicating potential underutilization and highlighting the need for improved migraine management strategies across all medical fields. Further efforts are necessary to optimize the use of triptans in treating migraines effectively.

Altered immunity in migraine: a comprehensive scoping review.

BACKGROUND: The pathogenesis of migraine remains unclear; however, a large body of evidence supports the hypothesis that immunological mechanisms play a key role. Therefore, we aimed to review current studies on altered immunity in individuals with migraine during and outside attacks. METHODS: We searched the PubMed database to investigate immunological changes in patients with migraine. We then added other relevant articles on altered immunity in migraine to our search. RESULTS: Database screening identified 1,102 articles, of which 41 were selected. We added another 104 relevant articles. We found studies reporting elevated interictal levels of some proinflammatory cytokines, including IL-6 and TNF-α. Anti-inflammatory cytokines showed various findings, such as increased TGF-ß and decreased IL-10. Other changes in humoral immunity included increased levels of chemokines, adhesion molecules, and matrix metalloproteinases; activation of the complement system; and increased IgM and IgA. Changes in cellular immunity included an increase in T helper cells, decreased cytotoxic T cells, decreased regulatory T cells, and an increase in a subset of natural killer cells. A significant comorbidity of autoimmune and allergic diseases with migraine was observed. CONCLUSIONS: Our review summarizes the findings regarding altered humoral and cellular immunological findings in human migraine. We highlight the possible involvement of immunological mechanisms in the pathogenesis of migraine. However, further studies are needed to expand our knowledge of the exact role of immunological mechanisms in migraine pathogenesis.

Advances in Exercise in the Clinical Trials of Migraine: A Scoping Review.

PURPOSE OF REVIEW: This review aimed to investigate emerging evidence regarding the effectiveness of exercise for migraines, focusing on the results of recent trials. Additionally, it explored the possibility of exercise as a treatment for migraines. RECENT FINDINGS: Between 2020 and 2023, five, four, one, and two trials were conducted regarding the effect of aerobic exercise, anaerobic exercise, Tai Chi, and yoga, respectively, on migraine; all studies showed significant effects. Two trials on aerobic exercise showed that high-intensity exercise was similar to or slightly more effective than moderate-intensity exercise as a treatment for migraines. Three trials on anaerobic exercise reported its effectiveness in preventing migraines. Regarding efficacy, side effects, and health benefits, aerobic exercises and yoga are potentially beneficial strategies for the prevention of migraines. Further studies are needed to develop evidence-based exercise programs for the treatment of migraines.

Risk Factors and Temporal Patterns of Poststroke Epilepsy across Stroke Subtypes: Insights from a Nationwide Cohort Study in Korea.

INTRODUCTION: We aimed to investigate the risk factors associated with poststroke epilepsy (PSE) among patients with different subtypes of stroke, focusing on age-related risk and time-varying effects of stroke subtypes on PSE development. METHODS: A retrospective, nationwide, population-based cohort study was conducted using Korean National Health Insurance Service-National Sample Cohort data. Patients hospitalized with newly diagnosed stroke from 2005 to 2015 were included and followed up for up to 10 years. The primary outcome was the development of PSE, defined as having a diagnostic code and a prescription for anti-seizure medication. Multivariable Cox proportional hazard models were used to estimate PSE hazard ratios (HRs), and time-varying effects were also assessed. RESULTS: A total of 8,305 patients with ischemic stroke, 1,563 with intracerebral hemorrhage (ICH), and 931 with subarachnoid hemorrhage (SAH) were included. During 10 years of follow-up, 4.6% of patients developed PSE. Among patients with ischemic stroke, significant risk factors for PSE were younger age (HR = 1.47), living in rural areas (HR = 1.35), admission through the emergency room (HR = 1.33), and longer duration of hospital stay (HR = 1.45). Time-varying analysis revealed elevated HRs for ICH and SAH, particularly in the first 2 years following the stroke. The age-specific HRs also showed an increased risk for those under the age of 65, with a noticeable decrease in risk beyond that age. CONCLUSION: The risk of developing PSE varies according to stroke subtype, age, and other demographic factors. These findings underscore the importance of tailored poststroke monitoring and management strategies to mitigate the risk of PSE.

The association between migraine and Parkinson's disease: a nationwide cohort study.

OBJECTIVES: Clinical studies have suggested an association between migraine and the occurrence of Parkinson's disease (PD). However, it is unknown whether migraine affects PD risk. We aimed to investigate the incidence of PD in patients with migraine and to determine the risk factors affecting the association between migraine and PD incidence. METHODS: Using the Korean National Health Insurance System database (2002-2019), we enrolled all Koreans aged ≥40 years who participated in the national health screening program in 2009. International Classification of Diseases (10th revision) diagnostic codes and Rare Incurable Diseases System diagnostic codes were used to define patients with migraine (within 12 months of enrollment) and newly diagnosed PD. RESULTS: We included 214,193 patients with migraine and 5,879,711 individuals without migraine. During 9.1 years of follow-up (55,435,626 person-years), 1,973 (0.92%) and 30,664 (0.52%) individuals with and without migraine, respectively, were newly diagnosed with PD. Following covariate adjustment, patients with migraine showed a 1.35-fold higher PD risk than individuals without migraine. The incidence of PD was not significantly different between patients with migraine with aura and those without aura. In males with migraine, underlying dyslipidemia increased the risk of PD (p=0.012). In contrast, among females with migraine, younger age (<65 years) increased the risk of PD (p=0.038). CONCLUSIONS: Patients with migraine were more likely to develop PD than individuals without migraine. Preventive management of underlying comorbidities and chronic migraine may affect the incidence of PD in these patients. Future prospective randomized clinical trials are warranted to clarify this association.

Development and validation of an artificial intelligence model for the early classification of the aetiology of meningitis and encephalitis: a retrospective observational study.

Background: Early diagnosis and appropriate treatment are essential in meningitis and encephalitis management. We aimed to implement and verify an artificial intelligence (AI) model for early aetiological determination of patients with encephalitis and meningitis, and identify important variables in the classification process. Methods: In this retrospective observational study, patients older than 18 years old with meningitis or encephalitis at two centres in South Korea were enrolled for development (n = 283) and external validation (n = 220) of AI models, respectively. Their clinical variables within 24 h after admission were used for the multi-classification of four aetiologies including autoimmunity, bacteria, virus, and tuberculosis. The aetiology was determined based on the laboratory test results of cerebrospinal fluid conducted during hospitalization. Model performance was assessed using classification metrics, including the area under the receiver operating characteristic curve (AUROC), recall, precision, accuracy, and F1 score. Comparisons were performed between the AI model and three clinicians with varying neurology experience. Several techniques (eg, Shapley values, F score, permutation feature importance, and local interpretable model-agnostic explanations weights) were used for the explainability of the AI model. Findings: Between January 1, 2006, and June 30, 2021, 283 patients were enrolled in the training/test dataset. An ensemble model with extreme gradient boosting and TabNet showed the best performance among the eight AI models with various settings in the external validation dataset (n = 220); accuracy, 0.8909; precision, 0.8987; recall, 0.8909; F1 score, 0.8948; AUROC, 0.9163. The AI model outperformed all clinicians who achieved a maximum F1 score of 0.7582, by demonstrating a performance of F1 score greater than 0.9264. Interpretation: This is the first multiclass classification study for the early determination of the aetiology of meningitis and encephalitis based on the initial 24-h data using an AI model, which showed high performance metrics. Future studies can improve upon this model by securing and inputting time-series variables and setting various features about patients, and including a survival analysis for prognosis prediction. Funding: MD-PhD/Medical Scientist Training Program through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea.

Fast and flexible estimation of effective migration surfaces.

Spatial population genetic data often exhibits 'isolation-by-distance,' where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al., 2016 developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here, we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field model in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). With simulations, we show conditions under which FEEMS can accurately recover effective migration surfaces with complex gene-flow histories, including those with anisotropy. We apply FEEMS to population genetic data from North American gray wolves and show it performs favorably in comparison to EEMS, with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.

Estimating the spectrum in computed tomography via Kullback-Leibler divergence constrained optimization.

PURPOSE: We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x-ray spectrum that can accurately model the x-ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system. METHODS: Spectrum estimation is posed as an optimization problem with x-ray spectrum as unknown variables, and a Kullback-Leibler (KL)-divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process. The formulated constrained optimization problem is convex and can be solved efficiently by use of the exponentiated-gradient (EG) algorithm. We demonstrate the effectiveness of the proposed approach on the simulated and experimental data. The comparison to the expectation-maximization (EM) method is also discussed. RESULTS: In simulations, the proposed algorithm is seen to yield x-ray spectra that closely match the ground truth and represent the attenuation process of x-ray photons in materials, both included and not included in the estimation process. In experiments, the calculated transmission curve is in good agreement with the measured transmission curve, and the estimated spectra exhibits physically realistic looking shapes. The results further show the comparable performance between the proposed optimization-based approach and EM. CONCLUSIONS: Our formulation of a constrained optimization provides an interpretable and flexible framework for spectrum estimation. Moreover, a KL-divergence constraint can include a prior spectrum and appears to capture important features of x-ray spectrum, allowing accurate and robust estimation of x-ray spectrum in CT imaging.