Koirala Nabin, Adhikari Shishir Raj, Adhikari Mukesh, Yadav Taruna, Anwar Abdul Rauf, Ciolac Dumitru, Shrestha Bibhusan, Adhikari Ishan, Khanal Bishesh, Muthuraman Muthuraman
School of Medicine, Yale University, New Haven, CT 06511, USA.
Brain Imaging Research Core, University of Connecticut, Storrs, CT 06269, USA.
Brain Sci. 2025 May 1;15(5):481. doi: 10.3390/brainsci15050481.
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.
癫痫是世界上最常见的神经系统疾病之一，影响着约5000万人，在低收入和中等收入国家（LMICs）的患病率明显不成比例。令人担忧的是，每年超过80%的癫痫相关死亡发生在低收入和中等收入国家。使用伤残调整生命年（DALYs）评估的疾病负担表明，癫痫每年约占1300万个伤残调整生命年，由于诊断和治疗差距过高，低收入和中等收入国家承担了大部分负担。此外，低收入和中等收入国家还承受着巨大的经济负担，在某些情况下，癫痫的治疗成本高达国民生产总值（GNP）的0.5%。缺乏训练有素的医学专家使正确诊断和治疗变得更加困难。因此，在这种情况下，采用基于人工智能（AI）的解决方案可能会改善低收入和中等收入国家的癫痫护理。在这篇理论性和批判性综述中，我们关注低收入和中等收入国家的癫痫及其管理，以及人工智能技术在低收入和中等收入国家辅助癫痫护理方面的应用。我们首先对癫痫进行了总体介绍，并介绍了基本的诊断和治疗方法。然后，我们探讨了社会经济影响、治疗差距以及为缓解这些问题所做的努力。在此基础上，我们研究了近期与人工智能相关的发展及其作为低收入和中等收入国家临床应用辅助工具的潜力，以及未来方向的建议。我们在结论中提出，需要有可扩展的、低成本的人工智能解决方案，使其与当地基础设施、政策和社区参与相结合，以改善低收入和中等收入国家的癫痫护理。
