Artificial Intelligence in Stroke Imaging: Recent Advancements
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 12
Special Issue Editor
Interests: diagnosis and therapy of neurovascular diseases; acute ischemic stroke; digital imaging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) has emerged as a transformative force in stroke imaging, leveraging advanced techniques such as convolutional neural networks (CNNs) and deep learning (DL) to enhance diagnostic accuracy, treatment planning, and prognostic prediction. By automating tasks like infarct/hemorrhage detection, large vessel occlusion (LVO) identification, and Alberta Stroke Program Early CT Score (ASPECTS) grading, AI streamlines clinical workflows, reduces inter-observer variability, and enables faster, more precise decision-making in acute stroke management. This technology addresses critical challenges in stroke care, including time-sensitive interventions and resource allocation, ultimately improving patient outcomes.
This Special Issue aims to bridge the gap between AI innovation and clinical practice by showcasing cutting-edge research on AI-driven stroke imaging tools. It invites contributions from interdisciplinary fields, including radiology, neurology, computer science, and biomedical engineering, focusing on applications such as automated image segmentation, outcome prediction, and personalized treatment optimization. This Special Issue also explores challenges like data privacy, algorithm interpretability, and real-time integration into clinical systems, offering a platform for researchers and clinicians to collaborate on advancing AI-powered stroke care.
Dr. Philip Hoelter
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- stroke imaging
- deep learning
- machine learning
- convolutional neural networks
- large vessel occlusion
- prognostication
- automated diagnosis
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