About this Research Topic
Dementia, characterized by substantial cognitive decline and impairment of daily functioning, is a global health challenge that continues to increase. Recently, innovative intersections have arisen between the use of machine learning and cutting-edge tools, promising a significant breakthrough in the understanding and management of dementia and related conditions. This Research Topic will delve into the growing worldwide challenge of dementia and explore innovative approaches, emphasizing machine learning and advanced tools, for its prediction and treatment.
The goal of this Research Topic is to foster a comprehensive understanding and collaboration among researchers, clinicians, and experts to optimize outcomes for individuals affected by dementia-related conditions using the latest tools and technology.
This Research Topic aims to expand the horizons of our approach to dementia by:
-Explore Innovations in Machine Learning: Investigate the state of the art and innovations of machine learning techniques used for the prediction of dementia and related diseases and in drug discovery.
-Examine Cutting-Edge Tools: Discover the innovative array of leading-edge instruments including the latest neuroimaging technology, biomarkers, and genetic profiling with a focus on the identification of their role in the timely detection and individualized treatment.
-Enhance Early Detection: Focus on the improvisation and tuning of the techniques that use machine learning and sophisticated methods in the identification of dementia with a goal of early diagnosis and accurate detection.
-Understand Molecular Processes: Explore the molecular pathways at the core of dementia through the employment of the most advanced tools and techniques to unravel the disease mechanisms and the prime locations of intervention.
-Address Challenges: Raise and discuss the issues that need consideration when bringing the latest apps of machine learning on board as they pose some problems that need to be addressed. Aim to identify possible fixes and methods of improvement.
-Facilitate Knowledge Exchange: Provide a platform of knowledge sharing, in which researchers and practitioners can pool information, approaches, and experiences as a team for the combined understanding and treatment of dementia.
We welcome contributions that fall, but not limited to the following themes:
-Applications and implications of Deep Learning and Machine Learning in relation to dementia
-Utilization of Artificial Intelligence in Biomedical Applications for the prediction and treatment of dementia.
-Detailed exploration and application of Supervised Learning, Unsupervised Learning, and Data Mining in Machine Learning techniques.
-The intersection of Biomedical, Bioinformatics, Image Recognition, and Education Data Mining in understanding and combating dementia.
-Machine Learning in Alzheimer's Disease Drug Discovery, specifically the use of ML for biomarker prediction, drug target identification, virtual screening, high-throughput therapeutics identification, and integration of omics data for pathogenesis study.
Please be aware that manuscripts that do not employ tools relevant to Computational Neuroscience, should accordingly be submitted to the Medicine and Public Health section of Frontiers in Artificial Intelligence.
We invite original research articles, review articles, case reports, and brief reports that underscore advancements and novel insights connected to this topic. Contributions should be bountiful in knowledge that unravels complex understandings about dementia and its related diseases.
The goal of this Research Topic is to foster a comprehensive understanding and collaboration among researchers, clinicians, and experts to optimize outcomes for individuals affected by dementia-related conditions using the latest tools and technology.
This Research Topic aims to expand the horizons of our approach to dementia by:
-Explore Innovations in Machine Learning: Investigate the state of the art and innovations of machine learning techniques used for the prediction of dementia and related diseases and in drug discovery.
-Examine Cutting-Edge Tools: Discover the innovative array of leading-edge instruments including the latest neuroimaging technology, biomarkers, and genetic profiling with a focus on the identification of their role in the timely detection and individualized treatment.
-Enhance Early Detection: Focus on the improvisation and tuning of the techniques that use machine learning and sophisticated methods in the identification of dementia with a goal of early diagnosis and accurate detection.
-Understand Molecular Processes: Explore the molecular pathways at the core of dementia through the employment of the most advanced tools and techniques to unravel the disease mechanisms and the prime locations of intervention.
-Address Challenges: Raise and discuss the issues that need consideration when bringing the latest apps of machine learning on board as they pose some problems that need to be addressed. Aim to identify possible fixes and methods of improvement.
-Facilitate Knowledge Exchange: Provide a platform of knowledge sharing, in which researchers and practitioners can pool information, approaches, and experiences as a team for the combined understanding and treatment of dementia.
We welcome contributions that fall, but not limited to the following themes:
-Applications and implications of Deep Learning and Machine Learning in relation to dementia
-Utilization of Artificial Intelligence in Biomedical Applications for the prediction and treatment of dementia.
-Detailed exploration and application of Supervised Learning, Unsupervised Learning, and Data Mining in Machine Learning techniques.
-The intersection of Biomedical, Bioinformatics, Image Recognition, and Education Data Mining in understanding and combating dementia.
-Machine Learning in Alzheimer's Disease Drug Discovery, specifically the use of ML for biomarker prediction, drug target identification, virtual screening, high-throughput therapeutics identification, and integration of omics data for pathogenesis study.
Please be aware that manuscripts that do not employ tools relevant to Computational Neuroscience, should accordingly be submitted to the Medicine and Public Health section of Frontiers in Artificial Intelligence.
We invite original research articles, review articles, case reports, and brief reports that underscore advancements and novel insights connected to this topic. Contributions should be bountiful in knowledge that unravels complex understandings about dementia and its related diseases.
Keywords: Dementia, Machine Learning, Neuroimaging Technology, Alzhemiers
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.
