Basem Jade, Mani Racheed, Sun Scott, Gilotra Kevin, Dianati-Maleki Neda, Dashti Reza
Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States.
Department of Neurology, Stony Brook University Hospital, Stony Brook, NY, United States.
Front Cardiovasc Med. 2025 Apr 3;12:1525966. doi: 10.3389/fcvm.2025.1525966. eCollection 2025.
Neurocardiology is an evolving field focusing on the interplay between the nervous system and cardiovascular system that can be used to describe and understand many pathologies. Acute ischemic stroke can be understood through this framework of an interconnected, reciprocal relationship such that ischemic stroke occurs secondary to cardiac pathology (the Heart-Brain axis), and cardiac injury secondary to various neurological disease processes (the Brain-Heart axis). The timely assessment, diagnosis, and subsequent management of cerebrovascular and cardiac diseases is an essential part of bettering patient outcomes and the progression of medicine. Artificial intelligence (AI) and machine learning (ML) are robust areas of research that can aid diagnostic accuracy and clinical decision making to better understand and manage the disease of neurocardiology. In this review, we identify some of the widely utilized and upcoming AI/ML algorithms for some of the most common cardiac sources of stroke, strokes of undetermined etiology, and cardiac disease secondary to stroke. We found numerous highly accurate and efficient AI/ML products that, when integrated, provided improved efficacy for disease prediction, identification, prognosis, and management within the sphere of stroke and neurocardiology. In the focus of cryptogenic strokes, there is promising research elucidating likely underlying cardiac causes and thus, improved treatment options and secondary stroke prevention. While many algorithms still require a larger knowledge base or manual algorithmic training, AI/ML in neurocardiology has the potential to provide more comprehensive healthcare treatment, increase access to equitable healthcare, and improve patient outcomes. Our review shows an evident interest and exciting new frontier for neurocardiology with artificial intelligence and machine learning.
神经心脏病学是一个不断发展的领域，专注于神经系统和心血管系统之间的相互作用，可用于描述和理解多种病理状况。急性缺血性中风可通过这种相互关联、相互作用的关系框架来理解，即缺血性中风继发于心脏病变（心-脑轴），而心脏损伤继发于各种神经疾病过程（脑-心轴）。及时评估、诊断和后续治疗脑血管疾病和心脏疾病是改善患者预后和医学发展的重要组成部分。人工智能（AI）和机器学习（ML）是强大的研究领域，可帮助提高诊断准确性和临床决策，以更好地理解和管理神经心脏病学疾病。在本综述中，我们确定了一些广泛使用和即将出现的AI/ML算法，用于一些最常见的中风心脏来源、病因不明的中风以及中风继发的心脏疾病。我们发现了许多高度准确和高效的AI/ML产品，将它们整合后，在中风和神经心脏病学领域为疾病预测、识别、预后和管理提供了更高的效能。在隐源性中风方面，有前景的研究阐明了可能的潜在心脏病因，从而改善了治疗选择和二级中风预防。虽然许多算法仍需要更大的知识库或人工算法训练，但神经心脏病学中的AI/ML有潜力提供更全面的医疗保健治疗，增加公平医疗保健的可及性，并改善患者预后。我们的综述显示了人工智能和机器学习在神经心脏病学领域有着明显的研究兴趣和令人兴奋的新前沿。
