About this Research Topic
Cerebrovascular diseases are a leading cause of morbidity and mortality globally. Cerebrovascular diseases encompass conditions such as ischemic stroke, hemorrhagic stroke, arteriovenous malformations, arteriovenous fistulae, central nervous system vascular tumors, and idiopathic intracranial hypertension. Recent advancements in artificial intelligence (AI) within the healthcare and medical sectors have spurred the integration of machine learning and data science to enhance clinical research and patient outcomes. AI applications in cerebrovascular disease management aim to achieve a variety of objectives, including the adoption of novel diagnostic tools, accurate outcome prediction, treatment optimization, and the advancement of precision medicine in treatment protocols. Additionally, AI seeks to establish significant clinical correlations.
The aim of this research topic is to foster innovation in the field of cerebrovascular diseases through the application of machine learning and AI technologies by compiling advances in research that investigate how AI can be utilized to improve the diagnosis and therapeutic interventions of cerebrovascular diseases. The broader goal is to identify opportunities where the application of AI could optimize cerebrovascular disease management in terms of early detection, improved surgical procedures, prognosis prediction, and personalized treatment strategies. By exploring these potentials, we aim to foster a dialogue within the research community about the future of stroke management in the age of AI.
The scope of research articles is not limited to ischemic stroke, hemorrhagic stroke, arteriovenous malformations, arteriovenous fistulae, central nervous system vascular tumors, and idiopathic intracranial hypertension, we also welcome neurovascular associated conditions from malignancies and other systemic diseases affecting the cerebrovascular system. We invite submissions of all article types accepted by Frontiers in Neurology pertaining but not limited to the following themes:
• Application of AI in early detection and diagnosis of cerebrovascular diseases
• Use of AI algorithms in predicting disease progression in cerebrovascular patients
• Role of AI in the formulation of personalized treatment plans
• Role of machine learning in identifying new risk factors for cerebrovascular diseases
The aim of this research topic is to foster innovation in the field of cerebrovascular diseases through the application of machine learning and AI technologies by compiling advances in research that investigate how AI can be utilized to improve the diagnosis and therapeutic interventions of cerebrovascular diseases. The broader goal is to identify opportunities where the application of AI could optimize cerebrovascular disease management in terms of early detection, improved surgical procedures, prognosis prediction, and personalized treatment strategies. By exploring these potentials, we aim to foster a dialogue within the research community about the future of stroke management in the age of AI.
The scope of research articles is not limited to ischemic stroke, hemorrhagic stroke, arteriovenous malformations, arteriovenous fistulae, central nervous system vascular tumors, and idiopathic intracranial hypertension, we also welcome neurovascular associated conditions from malignancies and other systemic diseases affecting the cerebrovascular system. We invite submissions of all article types accepted by Frontiers in Neurology pertaining but not limited to the following themes:
• Application of AI in early detection and diagnosis of cerebrovascular diseases
• Use of AI algorithms in predicting disease progression in cerebrovascular patients
• Role of AI in the formulation of personalized treatment plans
• Role of machine learning in identifying new risk factors for cerebrovascular diseases
Keywords: artificial intelligence, AI, cerebrovascular disease, diagnosis, interventional procedures, stroke, aneurysms, arteriovenous malformations, idiopathic intracranial hypertension, machine learning, data science, big data, neurointervention, neuroendovascular
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
