Comprehensive Summary
Preclinical epilepsy research has historically used animal models to determine the mechanisms behind epilepsy as well as possible treatments. In recent years, artificial intelligence (AI) has become a promising research tool in neuroscience due to its adaptability. This review by Medel-Matus et al. summarizes the ways that AI has been used in epilepsy research. AI use has been divided into three main areas: diagnosis of seizures, identifications of health disorders associated with epilepsy, and exploration of new treatments. AI can aid in the diagnosis of seizures by using EEG experimental data from previous seizures and key biomarkers. Manual analysis of EEG data is tedious and time-consuming, so using AI is an effective way to increase the accuracy of identifying, characterizing, and predicting the occurrence of seizures. Additionally, identifying comorbidities of epilepsy can help understand the causes of epilepsy and its relationship with other disorders. Through behavior monitoring and video analysis, AI can determine the presence of comorbidities within epilepsy treatment. Finally, AI uses machine learning (ML) models to run simulations of potential treatments and perform computational analyses to evaluate the efficacy of experimental treatments, such as novel pharmaceutical agents. In conclusion, AI in epilepsy research has the potential to enhance our understanding of the pathophysiology of epilepsy, aid in diagnosing epilepsy, and facilitate the development of more effective treatments.
Outcomes and Implications
Medel-Matus et al.’s study has important medical implications for epilepsy, from diagnosis to developing treatment options. AI’s ability to analyze large datasets quickly and accurately has been instrumental in predictive diagnosis and categorization of seizures. Additionally, the study mentions the use of AI in researching brain tissue alterations after epileptic events. This demonstrates a promising new avenue of epilepsy research that uses both advanced brain imaging and ML models to discover the effects of seizures. Furthermore, the drug development process has the potential to become highly expedited by the use of AI. ML models can run simulations that perform the entire process from target identification to clinical trial design. Although there are immense benefits to using AI in epilepsy research, Medel-Matus et al. note that future studies must be done to resolve issues such as lack of datasets, excessive computational demands, and medical devices failures.