About this Research Topic
As the world’s population ages rapidly, dementia is becoming a major global health problem. Although there are drugs, such as cholinesterase inhibitors, used to temporarily improve the symptoms of dementia, no currently available treatments can cure it. Therefore, the emphasis is now placed on the prevention of the onset of dementia. To this end, it is important to establish diagnostic technologies for the early detection of cognitive impairment. To resolve these issues, this Research Topic welcomes papers that apply machine learning technologies, including ”deep learning”, to the detection of cognitive impairment and dementia.
Deep learning (DL) is a subset of machine learning based on modern neural networks and analyzes regularity and relevance from a large amount of complex data, which allows judgments and predictions to be made. In recent years, this branch of data science has progressed in quantum leaps, generating innovation in various fields including medicine. In the medical field, machine learning has been successfully applied in various areas including cancer diagnosis, image-based diagnosis, genetics-based diagnosis, and prediction of treatment outcomes. Most importantly it has been shown, in many cases, to outperform the diagnostic ability of human experts. However, the application of DL to the diagnosis of dementia and risk assessment of cognitive impairment is relatively limited compared to other medical fields.
This Research Topic aims to encourage clinicians working with dementia patients to deepen their understanding of machine learning, including DL, which is expected to stimulate the discovery of new methods and applications. Additionally, we broaden our interests to the entire healthcare domains including wellness, as well as medicine. Having said this, we consider contributions not only based on clinical data collected and extracted for medical care purposes but also based on other data sensed and collected for self-wellness management such as Fitbit and various wearable sensors.
We call for Original Research and Reviews on the uses of machine learning, including but not necessarily limited to deep learning, in the medical context, as well as in context related to self-wellness management, towards the diagnosis of dementia and risk assessment of cognitive declines, such as the classification, estimation, analysis, and prediction using:
• Imaging data (e.g. MRI, PET, CT):
• Blood data;
• Physiological data (e.g., EEG, NIRS);
• Data relating to behaviors including but not limited to facial expressions, voice, etc.
Volume I: https://www.frontiersin.org/research-topics/12307/application-of-machine-learning-in-the-diagnosis-of-dementia#overview
Deep learning (DL) is a subset of machine learning based on modern neural networks and analyzes regularity and relevance from a large amount of complex data, which allows judgments and predictions to be made. In recent years, this branch of data science has progressed in quantum leaps, generating innovation in various fields including medicine. In the medical field, machine learning has been successfully applied in various areas including cancer diagnosis, image-based diagnosis, genetics-based diagnosis, and prediction of treatment outcomes. Most importantly it has been shown, in many cases, to outperform the diagnostic ability of human experts. However, the application of DL to the diagnosis of dementia and risk assessment of cognitive impairment is relatively limited compared to other medical fields.
This Research Topic aims to encourage clinicians working with dementia patients to deepen their understanding of machine learning, including DL, which is expected to stimulate the discovery of new methods and applications. Additionally, we broaden our interests to the entire healthcare domains including wellness, as well as medicine. Having said this, we consider contributions not only based on clinical data collected and extracted for medical care purposes but also based on other data sensed and collected for self-wellness management such as Fitbit and various wearable sensors.
We call for Original Research and Reviews on the uses of machine learning, including but not necessarily limited to deep learning, in the medical context, as well as in context related to self-wellness management, towards the diagnosis of dementia and risk assessment of cognitive declines, such as the classification, estimation, analysis, and prediction using:
• Imaging data (e.g. MRI, PET, CT):
• Blood data;
• Physiological data (e.g., EEG, NIRS);
• Data relating to behaviors including but not limited to facial expressions, voice, etc.
Volume I: https://www.frontiersin.org/research-topics/12307/application-of-machine-learning-in-the-diagnosis-of-dementia#overview
Keywords: Machine Learning, deep learning, dementia, imaging
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.
