About this Research Topic
This Research Topic aims to present a collection of articles that help address the above challenges faced in applying artificial intelligence to cerebrovascular diseases. Cerebrovascular disease is the most common life-threatening neurological event and is the second leading cause of death worldwide. Artificial intelligence has the potential to aid the early diagnosis of cerebrovascular disease and provide better risk management for patients. This Research Topic will cover recent developments in machine learning and deep learning methods for detecting cerebrovascular disease using imaging and clinical data. Methods that attempt to improve the versatility of algorithms in the early screening of cerebrovascular diseases are highly supported. This Research Topic also aims to investigate how to improve artificial intelligence's generalizability and robustness in diagnosing cerebrovascular diseases. We are also interested in methods that will enhance deep learning models' interpretability and better integration of human and machine intelligence.
Potential topics include but are not limited to the following:
- Simultaneous detection and screening of multiple cerebrovascular diseases
- Multimodal data integration for cerebrovascular disease diagnosis
- Explainable artificial intelligence for risk assessment of cerebrovascular diseases
- Robust and generalizable artificial intelligence models for cerebrovascular diseases
- Novel computational methods and integration with artificial intelligence for cerebrovascular hemodynamic analysis
- Novel imaging biomarker discovery using artificial intelligence
- Novel imaging processing to aid cerebrovascular disease analysis
- Neurointerventional education and skill improvement
Keywords: Artificial Intelligence, Cerebrovascular Disease, Explainable Artificial Intelligence, Deep Learning, Computational Method, Simulation, Hemodynamics, Aneurysm, Atherosclerosis, AVM, Moyamoya, Large Vessel Occlusion
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.