The Future of Epilepsy Management: Advancements in AI Diagnosis, Treatment, and Prognosis

Background and aims: The incidence of epilepsy, a neurological disorder, is increasing. Thecauses of epilepsy vary among individuals and include congenital gene mutations, traumatic injury,and infections. The heterogeneity of the causes has made it difficult to diagnose and selectappropriate treatments accurately. The diagnosis of epilepsy is based on a combination of EEGsignals, biological markers, clinical data, and other information recorded from the patient, whichare classified into epileptic and normal cases. The recent surge in medical data and computingpower has facilitated an increase in big data analysis and AI studies in the field of epilepsy. Thisarticle provides an overview of epilepsy, big data, and AI, and reviews the use of a common datamodel for seizure detection, epilepsy treatment, and forecasting through big data analysis.Method: To collect data related to the topic, we conducted a search of Google Scholar, NCBI, andScopus databases, and identified relevant articles including case reports, case series, case-controlstudies, reviews, editorials, and cohort studies. We then extracted relevant information from thesesources.Results: Understanding an Electroencephalography (EEG) signal and clinical data with accuracycan be a monotonous and time-consuming task. To address the challenges in this field, Artificialintelligence (AI) methods, like machine learning and deep learning, are being used more frequentlyto manage the vast amounts of data. AI applications play a vital role in automating seizuredetection, pre-surgical planning, predicting medication response, and forecasting medical andsurgical outcomes from the examination of EEG, clinical data, and other imaging. Computationalstudies are making significant advancements in precise diagnosis of seizure types and determiningthe best treatment for patients with epilepsy. These studies are separated into two categories: ۱)studies using artificial intelligence that employ computational machines with particular functions,such as machine learning methods based on large amounts of data from several patients for automaticdiagnosis and prognosis prediction for individual patients, and ۲) patient-specific modeling-based studies that use biophysical in-silico platforms to understand pathological mechanismsand find the best treatment for each patient using their individual data by reproducing their brainnetwork dynamics. These computational approaches are valuable as they can merge various typesof data gathered from patients and analysis results into a single platform, presenting a new paradigmfor precision medicine if implemented effectively.Conclusion: Artificial intelligence (AI) is not only useful for analyzing medical data to preventdiseases, diagnose them, monitor patients, and develop new protocols, but it can also help cliniciansmanage large volumes of data more precisely and efficiently. This can enable patients tomonitor seizures before they occur and aid doctors in their diagnosis and treatment. As computationalcapabilities improve, more effective machine learning algorithms become available, andlarger datasets accumulate, clinicians and researchers will increasingly reap the benefits of beingfamiliar with these techniques and the significant progress already made in their application inepilepsy.

کلیدواژه ها:

Artificial Intelligence ، epilepsy ، personalized therapies ، predicting prognosis ، AI algorithms