- Division of Neurosurgery, Department of Surgery, Taipei Tzu Chi Hospital (TCH), Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan; Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University (CCU), Taipei, TaiwanInterests: Neurosurgery; Neurological Disorders; Clinical Neuroscience; Evidence-based Medicine; Biomedical Image/Signal
- Department of Health Informatics, College of Public Health and Health Informatics, Ha'il University, Ha'il, Kingdom of Saudi ArabiaInterests: Health Informatics; Disease Surveillance Especially of Infectious Diseases; Spatial Epidemiology; Health Information Systems; Electronic Health Records; Nursing Informatics; Role of Social Media in Changing Healthcare Landscape
The number of older adults is expected to double in next two decades globally. This advancing age of the population is likely to directly correlate to a rise in the burden of age-related cardiovascular, cardiopulmonary, cerebrovascular, neurotrauma, neurodegenerative diseases, and malignancies. As older adults tend to account for a disproportionate greater allocation of healthcare resources, which is a finite, optimization of healthcare delivery efficiency will be crucial to ensure a sustainable future. Therefore, precise prediction, early detection, continuous monitoring, and effective prevention of chronic diseases with modern medical technologies, particularly computer-assisted tools for elderly people are noticeably essential in an advance urban society.
Artificial intelligence (AI) application has been proposed to be an emerging solution to improve healthcare delivery efficiency in a number of complex settings in the elderly such as disease classification, risk prediction, quality control, decision support, and treatment effect estimation. Other modern techniques like image/signal processing and analysis may also be helpful. Alternative therapies involving radiotherapy and traditional Chinese medicine (TCM)/acupunctures are worth to be discussed. For instance, radiomic techniques and deep learning methods based on deep belief network (DBN), convolution neural network (CNN), recurrent neural network (RNN) were widely used in computer-aided diagnosis for medical image analysis in various central nervous system (CNS) pathologies, even for small malignant nodosities detection. In the field of cardiovascular medicine, promising findings have been reported in the application of machine learning in echocardiographic, cardiac magnetic resonance image processing, and arrhythmia prediction.
This Special Section in Frontiers in Bioscience-Landmark seeks to provide a platform to review the contemporary status of AI application as well as future potential developments in the areas related to the aging population. Both clinical together with basic experimental and theoretical studies including original and meta-analysis research papers and/or review articles are welcomed.
The topics of interest include, but are not limited to:
· Prospects of AI and/or novel deep learning advancements for early disease detection, risk stratification, disease prevention, and decision support tools for clinical decision making in cardiovascular, cardiopulmonary, cerebrovascular, neurotrauma, neurodegenerative diseases, and malignancies
· Prospects of medical image/signal processing/analysis, application of big data analytics, and other statistical modeling approaches for data mining in cardiovascular, cardiopulmonary, cerebrovascular, neurotrauma, neurodegenerative diseases, and malignancies
· Application of other alternative approaches such as radiotherapy, and TCM/acupunctures in an innovative fashion to optimize the treatment efficacy to the aging population
Assoc. Prof. Woon-Man Kung, Assoc. Prof. Jiehui Jiang, Asst. Prof. Suleman Atique, Ming-yu (Anthony) Chuang, Assoc. Prof. Ajit Kumar and Prof. Chih-Yu Hsu
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