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Purpose: Alcohol is implicated in epileptogenesis and seizures attack. An increasing number of studies about alcohol and epilepsy have been published. We aimed to assess research trends and hot spots in the field of alcohol and epilepsy. Patients and Methods: Literature concerning alcohol and epilepsy was systemically searched through the Web of Science database. Collaborative maps were quantitatively analyzed by using the VOSviewer and CiteSpace tools. Results: A total of 1578 papers about the field of alcohol and epilepsy were taken into analysis, which was written by 6840 authors from 2153 institutions in 85 countries, published in 676 journals, and cited 79 667 references from 10 750 journals. The United States was the leading country and had close ties with others. The University of Toronto was the most productive institution. Alcoholism‐clinical and experimental research was the fastest‐growing journal. Richard J. Bodnar was the author contributing the most literature. Analysis of keywords showed epilepsy, alcohol, seizures, alcohol withdrawal, and management were common themes. Conclusion: The presented study conducted the first bibliometric analysis of the field of alcohol and epilepsy, which will provide insights into the latest progress, evolution paths, frontier research hot spots, and future research trends in the field. [ABSTRACT FROM AUTHOR]

Computer vision (CV) shows increasing promise as an efficient, low‐cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research. [ABSTRACT FROM AUTHOR]

Aims/Introduction: An overrepresentation of epilepsy has been suggested in patients with type 1 diabetes (T1D). This meta-analysis was conducted to evaluate if type 1 diabetes is associated with a higher incidence of epilepsy. Materials and Methods: Longitudinal observational studies which are relevant to the purpose of the meta-analysis were screened and obtained by searching PubMed, Embase, and Web of Science databases. Random-effects models were used when significant heterogeneity was observed; otherwise, fixed-effects models were used. Results: Six observational studies involving 10 datasets of 8,001,899 participants were included, with six datasets including children and only one dataset including older people. Among them, 100,414 (1.25%) had type 1 diabetes. During the follow-up duration of 5.4-15.2 years (mean: 9.5 years), 98,644 cases (1.23%) of epilepsy were observed. Compared with participants with normoglycemia, those with type 1 diabetes were shown to have a higher incidence of epilepsy (risk ratio [RR]: 2.41, 95% confidence interval 1.69-3.44, P < 0.001; I2 = 95%) after adjustment of potential confounding variables including age and sex. Subgroup analysis showed consistent results in nested case-control and retrospective cohort studies, and in studies of children, non-elderly adult, and older participants (P for subgroup difference = 0.42 and 0.07). In addition, a stronger association of type 1 diabetes and epilepsy was suggested in studies with follow-up duration <10 years compared with those =10 years (RR: 3.34 vs 1.61, P for subgroup difference < 0.001). Conclusion: Patients with type 1 diabetes may have a higher risk of epilepsy, which was mainly driven by datasets including children. [ABSTRACT FROM AUTHOR]