Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries

Abstract

Epilepsy, one of the most common neurological diseases in the world, affects around 50 million people, with a notably disproportionate prevalence in individuals residing in low- and middle-income countries (LMICs). Alarmingly, over 80% of annual epilepsy-related fatalities occur within LMICs. The burden of the disease assessed using Disability Adjusted Life Years (DALYs) shows that epilepsy accounts for about 13 million DALYs per year, with LMICs bearing most of this burden due to the disproportionately high diagnostic and treatment gaps. Furthermore, LMICs also endure a significant financial burden, with the cost of epilepsy reaching up to 0.5% of the Gross National Product (GNP) in some cases. Difficulties in the appropriate diagnosis and treatment are complicated by the lack of trained medical specialists. Therefore, in these conditions, adopting artificial intelligence (AI)-based solutions may improve epilepsy care in LMICs. In this theoretical and critical review, we focus on epilepsy and its management in LMICs, as well as on the employment of AI technologies to aid epilepsy care in LMICs. We begin with a general introduction of epilepsy and present basic diagnostic and treatment approaches. We then explore the socioeconomic impact, treatment gaps, and efforts made to mitigate these issues. Taking this step further, we examine recent AI-related developments and their potential as assistive tools in clinical application in LMICs, along with proposals for future directions. We conclude by suggesting the need for scalable, low-cost AI solutions that align with the local infrastructure, policy and community engagement to improve epilepsy care in LMICs.

Keywords: artificial intelligence; epilepsy; epilepsy care; epilepsy eiagnosis; low and middle income countries.

Figure 1

Seizure classification (modified from [8]).

Figure 2

Brain states related to seizure occurrence and the artificial intelligence (AI)-based techniques most frequently applied to predict, detect and classify seizures. Convolutional Neural Networks, Support Vector Machines, and K-Nearest Neighbors are the most popular techniques used for seizure prediction [65]. For seizure detection, one- and two-dimensional convolutional neural networks, Recurrent Neural Networks, Support Vector Machines, and Random Forests are the most widely used models [66], while for seizure classification, convolutional neural network, Support Vector Machine, and K-Nearest Neighbor (KNN) algorithms take precedence.

Figure 3

General pipeline of a seizure prediction model.