ArticleAlzheimer's research & therapy2024
Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum.
Article in Alzheimer's research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The effects of traditional mind-body exercises on cognitive function in neurodegenerative diseases or prodromal cognitive decline: a meta-analysis.Frontiers in public health · 2026Pooled it
- Predictive value of the Alzheimer polygenic risk score on cognitive decline in patients with mild cognitive impairment and Alzheimer's disease dementia.The journal of prevention of Alzheimer's disease · 2026Article
- Applying Multiple Machine Learning Models to Classify Mild Cognitive Impairment from Speech in Community-Dwelling Older Adults.Journal of Intelligence · 2026Article
- A Machine Learning Framework for Cognitive Impairment Screening from Speech with Multimodal Large Models.Bioengineering (Basel, Switzerland) · 2026Article
- A novel class-attention transformer-driven feature fusion technique-based speech disorder classification.Frontiers in medicine · 2026Article
- Listening for Alzheimer's clues: machine learning analysis of multidomain speech features for cognitive impairment screening.Frontiers in aging neuroscience · 2026Article
- Multi-modal data analysis for early detection of alzheimer's disease and related dementias.The journal of prevention of Alzheimer's disease · 2025Article
- Linguistic Markers in Spontaneous Speech: Insights into Subjective Cognitive Decline (Review).Healthcare (Basel, Switzerland) · 2025Review
- Feasibility of telephone and computerized cognitive testing as a secondary outcome in an acute stroke clinical trial: A mixed methods sub-study of the AcT Trial.European stroke journal · 2025Article
- Listening to the Mind: Integrating Vocal Biomarkers into Digital Health.Brain sciences · 2025Article
- Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.GeroScience · 2025Review
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Machine learning applied to mild cognitive impairment: bibliometric and visual analysis from 2015 to 2024.Frontiers in neurology · 2025Review
- Federated learning for cognitive impairment detection using speech data.Frontiers in artificial intelligence · 2025Article
- Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer's disease.Frontiers in artificial intelligence · 2025Article
- The COMFORTage project: study protocol for the integration of multiple sources towards personalised preventions at Ace Alzheimer Center Barcelona.Frontiers in digital health · 2025Article
- Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.Drug design, development and therapy · 2025Article
- Article
- FORTCARE-MCI study protocol: evaluation of Fortasyn Connect in the management of mild cognitive impairment in primary care.Frontiers in neurology · 2024Article
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21 authors.
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Abstract
backgroundAdvancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD spectrum.
methodsPhysical-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundació Ace (NBACE) using SS-derived information.
resultsThe results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial ability.
conclusionsIn this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols traditionally used to assess cognitive function. Overall, it opens up novel prospects for developing screening tools and remote disease monitoring.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.