Stroke continues to be a leading cause of death and disability worldwide, emphasizing the need for rapid detection and treatment to minimize its substantial health impact. Recently, advancements in technology, particularly in machine learning and artificial intelligence, have shown remarkable potential in addressing this challenge. These technologies can potentially revolutionize prediction, diagnosis, and treatment strategies. The increasing availability of high-dimensional data from myriad sources - including electronic health records, imaging, genomics, and wearable technology - provides rich resources for these algorithms.
The aim of this Research Topic is to gather and spotlight innovative applications of machine learning and other cutting-edge tools in predicting and treating stroke. We aim to provide a platform for researchers to share their advancements, thus accelerating the integration of these applications into clinical practice. We anticipate that the gathered works will generate a holistic understanding of the current landscape, identify gaps that still need to be addressed, and inspire the development of future technologies in stroke prediction and treatment.
This Research Topic aims to cover a wide spectrum of areas in machine learning and cutting-edge technology as applied to stroke prediction and treatment strategies. We invite manuscripts that focus on the following aspects:
-Design and development of machine learning algorithms specifically purposed for predicting stroke.
-Novel applications of artificial intelligence in different facets of stroke care, including but not limited to diagnosis, treatment, and follow-up processes.
-Integration and utilization of diverse data sources, from standard health records to novel biometric data from wearables in assessing stroke risk and administering personalized treatments.
-Studies evaluating the real-world clinical implications, effectiveness, and patient outcomes following the adoption of advanced technologies in stroke care settings.
-Exploration and utilization of publicly available datasets for the enhancement of stroke prediction systems.
-Investigations into ethical, legal, and social issues surrounding the use of these technologies in healthcare.
Please be aware that manuscripts dealing with stoke prediction and treatment, especially those that do not employ tools relevant to Computational Neuroscience, should accordingly be submitted to Frontiers in Stroke.
We welcome various manuscript types, highlighting original research, in-depth reviews, methodological advancements, and brief reports. All submissions should manifest innovation in stroke management, focusing on the role of machine learning and state-of-the-art technologies, and their potential or realized impact on clinical practice and patient outcomes.
Together, let's transform stroke prediction and treatment strategies for the future.
Stroke continues to be a leading cause of death and disability worldwide, emphasizing the need for rapid detection and treatment to minimize its substantial health impact. Recently, advancements in technology, particularly in machine learning and artificial intelligence, have shown remarkable potential in addressing this challenge. These technologies can potentially revolutionize prediction, diagnosis, and treatment strategies. The increasing availability of high-dimensional data from myriad sources - including electronic health records, imaging, genomics, and wearable technology - provides rich resources for these algorithms.
The aim of this Research Topic is to gather and spotlight innovative applications of machine learning and other cutting-edge tools in predicting and treating stroke. We aim to provide a platform for researchers to share their advancements, thus accelerating the integration of these applications into clinical practice. We anticipate that the gathered works will generate a holistic understanding of the current landscape, identify gaps that still need to be addressed, and inspire the development of future technologies in stroke prediction and treatment.
This Research Topic aims to cover a wide spectrum of areas in machine learning and cutting-edge technology as applied to stroke prediction and treatment strategies. We invite manuscripts that focus on the following aspects:
-Design and development of machine learning algorithms specifically purposed for predicting stroke.
-Novel applications of artificial intelligence in different facets of stroke care, including but not limited to diagnosis, treatment, and follow-up processes.
-Integration and utilization of diverse data sources, from standard health records to novel biometric data from wearables in assessing stroke risk and administering personalized treatments.
-Studies evaluating the real-world clinical implications, effectiveness, and patient outcomes following the adoption of advanced technologies in stroke care settings.
-Exploration and utilization of publicly available datasets for the enhancement of stroke prediction systems.
-Investigations into ethical, legal, and social issues surrounding the use of these technologies in healthcare.
Please be aware that manuscripts dealing with stoke prediction and treatment, especially those that do not employ tools relevant to Computational Neuroscience, should accordingly be submitted to Frontiers in Stroke.
We welcome various manuscript types, highlighting original research, in-depth reviews, methodological advancements, and brief reports. All submissions should manifest innovation in stroke management, focusing on the role of machine learning and state-of-the-art technologies, and their potential or realized impact on clinical practice and patient outcomes.
Together, let's transform stroke prediction and treatment strategies for the future.
