Prediction of individualized treatment in children with drug-resistant epilepsy based on clinical, genetic and neuroimmunological predictors using artificial intelligence

Drug-resistant epilepsy in children is one of the major challenges in modern pediatrics and neurology and is associated with recurrent epileptic seizures, impaired psychomotor and cognitive development, and reduced quality of life. Early identification of the risk of drug resistance and prediction of treatment response are important for selecting an individualized therapeutic strategy. This article discusses the potential of comprehensive assessment of clinical, genetic, and neuroimmunological characteristics of drug-resistant epilepsy in children and the application of artificial intelligence technologies to predict response to individualized treatment. Integration of clinical, genetic, immunological, electroencephalographic, and neuroimaging data is proposed to identify the risk of drug resistance and predict treatment effectiveness. This approach may contribute to improving early diagnosis and personalized treatment strategies in pediatric epilepsy.