About this Research Topic
Frontiers in Dementia is delighted to present the ‘Methods in’ series of article collections.
This Research Topic aims to highlight the latest experimental techniques and methods of Artificial Intelligence used to investigate fundamental questions in dementia research, including a) the study of risk factors and biomarkers of dementia, genetics and genomics of dementia-related outcomes; and b) the implementation of novel technologies and evaluations associated with dementia care burden. Review articles or opinions on methodologies or applications including the advantages and limitations of each are welcome. This Topic includes technologies and up-to-date methods which help advance science.
The contributions to this collection will undergo peer-review. Novelty may vary, but the utility of a method or protocol must be evident. We welcome contributions covering all aspects of the applications of Artificial Intelligence in dementia research.
This Research Topic welcomes:
• Methods: Describing either new or existing methods that are significantly improved or adapted for specific purposes. These manuscripts may include primary (original) data.
• Protocols: Detailed descriptions, including pitfalls and troubleshooting, to benefit those who may evaluate or employ the techniques. The protocols must be proven to work.
• Perspective or General Commentaries on methods and protocols relevant to dementia research.
• Reviews and mini-reviews of topical methods and protocols highlighting the important future directions of the field.
Dr. Kathleen Fraser was co-founder and currently holds shares of Winterlight Labs (a company aiming to assess cognitive status using speech). Dr. Fasih Haider is CEO and co-founder of Cosignity Ltd (company commercializing solutions that monitor a patient’s cognitive state and sleep quality). The other Topic Editors declare no competing interests with regard to the Research Topic subject.
This Research Topic aims to highlight the latest experimental techniques and methods of Artificial Intelligence used to investigate fundamental questions in dementia research, including a) the study of risk factors and biomarkers of dementia, genetics and genomics of dementia-related outcomes; and b) the implementation of novel technologies and evaluations associated with dementia care burden. Review articles or opinions on methodologies or applications including the advantages and limitations of each are welcome. This Topic includes technologies and up-to-date methods which help advance science.
The contributions to this collection will undergo peer-review. Novelty may vary, but the utility of a method or protocol must be evident. We welcome contributions covering all aspects of the applications of Artificial Intelligence in dementia research.
This Research Topic welcomes:
• Methods: Describing either new or existing methods that are significantly improved or adapted for specific purposes. These manuscripts may include primary (original) data.
• Protocols: Detailed descriptions, including pitfalls and troubleshooting, to benefit those who may evaluate or employ the techniques. The protocols must be proven to work.
• Perspective or General Commentaries on methods and protocols relevant to dementia research.
• Reviews and mini-reviews of topical methods and protocols highlighting the important future directions of the field.
Dr. Kathleen Fraser was co-founder and currently holds shares of Winterlight Labs (a company aiming to assess cognitive status using speech). Dr. Fasih Haider is CEO and co-founder of Cosignity Ltd (company commercializing solutions that monitor a patient’s cognitive state and sleep quality). The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Keywords: #MethodsIn;, artificial intelligence, dementia
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.
