Research Topic

Leveraging Machine and Deep Learning Technologies for Clinical Applications in Stroke Imaging

About this Research Topic

Machine and deep learning technologies, mining imaging, and clinical information have made outstanding progress in the understanding, diagnosis, and prognosis of stroke patients. State of the art approaches are capable of extracting, combining, and exploiting novel data representations and inference methods ...

Machine and deep learning technologies, mining imaging, and clinical information have made outstanding progress in the understanding, diagnosis, and prognosis of stroke patients. State of the art approaches are capable of extracting, combining, and exploiting novel data representations and inference methods to leverage prediction performance in many stroke applications. Data availability and curation, and effective ways to leverage longitudinal and sparse information are among the important areas requiring further interdisciplinary research.

In this Research Topic, we call for papers tackling challenges associated with data handling and its exploitation towards accurate and robust machine and deep learning systems for stroke imaging and stroke recovery prediction. Particularly, we welcome groups to submit novel scientific contributions utilizing clinical and/or neurological parameters obtained via machine and deep learning technologies, as well as novel machine and deep learning methodologies for stroke imaging applications, ranging from diagnosis, disease quantification, disease evolution, personalized stroke rehabilitation, and outpatient triaging.

Areas of interest include, but are not limited to the following:
• Classification/outcome prediction employing machine/deep learning technologies to clinical findings for information extraction and hypothesis testing.
• Novel machine/deep learning stroke neuroimaging methodologies for lesion quantification, decision support, and outcome prediction.
• Longitudinal stroke neuroimaging analysis.
• Multiomics stroke analysis, combining clinical, paraclinical, and imaging information.
• Personalized neuroimaging stroke rehabilitation technologies.
• Interhospital LVO triage.
• Image-based recovery assessment & treatment



The Editors would like to receive clinical, methodological, or translational research, in the form of Original Research, Opinions, Perspectives, or Reviews.


Keywords: Stroke Imaging, Stroke Recovery, Machine Learning, Deep Learning, Medical Image Analysis, Multi-Omics


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.

Recent Articles

Loading..

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

22 July 2021 Manuscript
15 October 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

22 July 2021 Manuscript
15 October 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..