Springer eBooks, Journal Year: 2023, Volume and Issue: unknown, P. 253 - 266
Published: Jan. 1, 2023
Language: Английский
Published: April 24, 2025
Language: Английский
Citations
0
Frontiers in Aging Neuroscience, Journal Year: 2024, Volume and Issue: 16
Published: May 9, 2024
Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative disorder characterized by diverse and prominent changes in behavior personality. One of the greatest challenges bvFTD to capture, measure predict its disease progression, due clinical, pathological genetic heterogeneity. Availability reliable outcome measures pivotal for future clinical trials monitoring. Detection change should be objective, clinically meaningful easily assessed, preferably associated with biological process. The purpose this scoping review examine status longitudinal studies bvFTD, evaluate current assessment tools propose potential progression markers. A systematic literature search (in PubMed Embase.com ) was performed. Literature on trajectories validity frequently-used organized five domains: global functioning, behavior, (social) cognition, neuroimaging fluid biomarkers. Evaluating data, we an adaptive battery, combining set sensitive markers, adjusted sporadic variants, adequate detection bvFTD.
Language: Английский
Citations
1
Journal of Neuropsychology, Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 2, 2024
Language: Английский
Citations
1
GeroScience, Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 17, 2024
Language: Английский
Citations
1
Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)
Published: Nov. 1, 2024
In super-aged societies, dementia has become a critical issue, underscoring the urgent need for tools to assess cognitive status effectively in various sectors, including financial and business settings. Facial speech features have been tried as cost-effective biomarkers of Alzheimer's disease (AD). We aimed establish an easy, automatic, extensive screening tool AD using chatbot artificial intelligence. Smile images visual auditory data natural conversations with from 99 healthy controls (HCs) 93 individuals or mild impairment due (PwA) were analyzed machine learning. A subset 8 facial 21 sound successfully distinguished PwA HCs, high area under receiver operating characteristic curve 0.94 ± 0.05. Another 20 predicted test scores, mean absolute error low 5.78 0.08. These results superior those obtained face alone conventional image depiction tasks. Thus, by combining spontaneous through chatbot, proposed model can be put practical use real-life scenarios.
Language: Английский
Citations
1
Healthcare, Journal Year: 2024, Volume and Issue: 12(21), P. 2194 - 2194
Published: Nov. 4, 2024
Background: Changes in the speech and language of patients with Alzheimer’s disease (AD) have been reported. Using machine learning to characterize these irregularities may contribute early, non-invasive diagnosis AD. Methods: We conducted cognitive function assessments, including Mini-Mental State Examination, 83 AD 75 healthy elderly participants, recorded pre- post-assessment conversations evaluate participants’ speech. analyzed characteristics spectrum, intensity, fundamental frequency, minute temporal variation (∆) intensity frequency compared them between participants. Additionally, we evaluated performance features that differed two groups as single explanatory variables. Results: found significant differences almost all elements spectrum groups. Regarding factors except for standard deviation In evaluation, areas under curve revealed by logistic regression analysis were higher center gravity (0.908 ± 0.036), mean skewness (0.904 0.023), kurtosis (0.932 (0.977 0.012) spectra. Conclusions: This study used reveal diagnosed comparison people. Significant components paving way early future.
Language: Английский
Citations
1
Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 336 - 357
Published: Feb. 14, 2024
Alzheimer's disease (AD) is a neurodegenerative disorder affecting people of more than 65 years age, though current health trends show these conditions appearing at earlier stages also. This may be attributed to lifestyle changes and increased exposure harmful chemicals. Once progressed later stages, brain-related disorders are very difficult treat. But, if detected earlier, they can managed better, prolonging the life patient. Today, AI bringing revolutionary in realm healthcare services. Now there tools that help early detection brain conditions, thus saving time patients doctors with increasing confidence test reports. These change way we perceive could scenario diagnosis AD from being high chance error prediction same degree precision.
Language: Английский
Citations
0
Alzheimer s & Dementia, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 5, 2024
Abstract INTRODUCTION Speech‐based testing shows promise for sensitive and scalable objective screening Alzheimer's disease (AD), but research to date offers limited evidence of generalizability. METHODS Data were taken from the AMYPRED (Amyloid Prediction in Early Stage Disease Acoustic Linguistic Patterns Speech) studies ( N = 101, 46 mild cognitive impairment [MCI]) Neuroimaging Initiative 4 (ADNI4) remote digital 426, 58 self‐reported MCI, AD or dementia) in‐clinic 57, 13 MCI) cohorts, which participants provided audio‐recorded responses automated story recall tasks Storyteller test battery. Text similarity, lexical, temporal, acoustic speech feature sets extracted. Models predicting early developed tested out sample demographically more diverse cohorts ADNI4 (> 33% historically underrepresented populations). RESULTS Speech models generalized well unseen data cohorts. The best‐performing evaluated text‐based metrics (text lexical features: area under curve 0.71–0.84 across cohorts). DISCUSSION predictions generalize samples. Highlights speech‐based is an prescreener (ADNI4). predictive (AD) 101). 57) 426) showed good generalization sample. evaluating text matching features most AD.
Language: Английский
Citations
0
Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring, Journal Year: 2024, Volume and Issue: 16(4)
Published: Oct. 1, 2024
Language: Английский
Citations
0
SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown
Published: Jan. 1, 2023
Extended reality (XR) technologies have experienced cycles of development - "summers" and "winters" for decades, but their overall trajectory is one increasing uptake. In recent years, immersive extended (IXR) applications, a kind XR that encompasses virtual (VR) augmented (AR) environments, become especially prevalent. The European Union (EU) exploring regulating this type technology, article seeks to support endeavor. It outlines safety privacy harms associated with IXR, analyzes what extent the existing EU framework digital governance including General Data Protection Regulation, Product Safety Legislation, ePrivacy Directive, Digital Markets Act, Services AI Act addresses these harms, offers some recommendations legislators on how fill regulatory gaps improve current approaches IXR.
Language: Английский
Citations
1