The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle-Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. The most common adult-onset dementia disorders occurring were Alzheimer's disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.
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http://dx.doi.org/10.3390/bioengineering9080370 | DOI Listing |
BMC Public Health
January 2025
Department of Statistics, Borana University, Borena, Oromia Region, Ethiopia.
Introduction: Hypertension is among the most significant non-communicable public health issues worldwide. High blood pressure, or hypertension, has been associated with severe health consequences, including death, aneurysms, stroke, chronic renal disease, eye damage, heart attack, heart failure, peripheral artery disease, and vascular dementia. Consequently, this study aimed to investigate the predictors linked to survival time and the progression of blood pressure measurements in hypertensive patients.
View Article and Find Full Text PDFBMC Geriatr
January 2025
School of Management, Shandong Second Medical University, Weifang, Shandong, China.
Background: Cognitive impairment is a common health problem among older adults. Previous studies have proven the association between sleep quality and cognitive impairment, but the specific underlying mechanisms need to be further explored. This study aimed to examine the relationship between sleep quality and cognitive impairment and the mediating effect of frailty in this relationship among the rural older adults.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden.
Accurate diagnosis and monitoring of neurodegenerative diseases require reliable biomarkers. Cerebrospinal fluid (CSF) proteins are promising candidates for reflecting brain pathology; however, their diagnostic utility may be compromised by natural variability between individuals, weakening their association with disease. Here, we measured the levels of 69 pre-selected proteins in cerebrospinal fluid using antibody-based suspension bead array technology in a multi-disease cohort of 499 individuals with neurodegenerative disorders including Alzheimer's disease (AD), behavioral variant frontotemporal dementia, primary progressive aphasias, amyotrophic lateral sclerosis (ALS), corticobasal syndrome, primary supranuclear palsy, along with healthy controls.
View Article and Find Full Text PDFTrends Cogn Sci
January 2025
Key Laboratory of Cognitive Science and Mental Health, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China. Electronic address:
Chronic pain (CP) not only causes physical discomfort but also significantly affects cognition. This review first summarizes emerging findings that reveal complex associations between CP and cognitive impairments, and then presents neuroimaging evidence showing aging-related brain alterations in CP and proposes a framework where accelerated brain aging links CP to cognitive impairments. This framework explains how CP-related multi-level factors, which either contribute to the onset of CP or arise as a result of CP, influence brain aging in linear and nonlinear ways, leading to cognitive impairments and increased dementia risk.
View Article and Find Full Text PDFJ Aging Phys Act
January 2025
Bournemouth University Clinical Research Unit, Faculty of Health & Social Sciences, Bournemouth University, Poole, United Kingdom.
Background/objectives: Adherence to exercise programs is required to reap their established benefits and to sustain Quality of Life (QoL). This study explored People Living with Dementia's (PLWD) adherence to a Tai Chi exercise program and its effects on their QoL. The study included assessment of factors affecting adherence to a Tai Chi exercise intervention, causes of nonadherence, and effect of adherence on PLWD's QoL.
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