ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2024
Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Machine Learning-Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- The myth of digital biomarkers in Alzheimer's disease: how to make them a reality.Current opinion in psychiatry · 2026Review
- Digital speech-based markers to advance prognosis in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Prediction of alzheimer's disease time to dementia onset using cross-sectional data from spatiotemporal biomarker progression patterns.European journal of nuclear medicine and molecular imaging · 2026Article
- Remote outcome measures in Alzheimer's disease clinical trials: A call to action.The journal of prevention of Alzheimer's disease · 2026Article
- Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.Alzheimer's research & therapy · 2026Article
- Psychometric modeling of Boston process approach data for dementia prediction in the Framingham Heart Study.Journal of the International Neuropsychological Society : JINS · 2026Article
- Assessment of Language Impairments Towards Identifying Markers for Early Diagnosis of Pathological Cognitive Decline.Behavioral sciences (Basel, Switzerland) · 2026Review
- Review
- Listening for Alzheimer's clues: machine learning analysis of multidomain speech features for cognitive impairment screening.Frontiers in aging neuroscience · 2026Article
- Automated MoCA scoring for Arabic speakers using hybrid AI of multimodal speech, vision, and LLM integration.Frontiers in psychology · 2026Article
- Artificial intelligence and machine learning for precision prevention of cognitive decline: integrating multimodal biomarkers, lifestyle interventions, and natural medicines from prediction to clinical practice.Frontiers in aging neuroscience · 2026Review
- Smart aging: integrating AI into elderly healthcare.BMC geriatrics · 2025Review
- Evaluating spoken language as a biomarker for automated screening of cognitive impairment.Communications medicine · 2025Article
- Multi-modal data analysis for early detection of alzheimer's disease and related dementias.The journal of prevention of Alzheimer's disease · 2025Article
- Speech digital biomarker combined with fluid biomarkers predict cognitive impairment through machine learning.Alzheimer's research & therapy · 2025Article
- Artificial intelligence-driven transformative applications in disease diagnosis technology.Medical review (2021) · 2025Review
- Reimagining Resilience in Aging: Leveraging AI/ML, Big Data Analytics, and Systems Innovation.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2025Article
- Developing an accessible dementia assessment tool: Leveraging a residual network, the trail making test, and demographic data.Journal of Alzheimer's disease : JAD · 2025Article
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7 authors.
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Abstract
introductionIdentification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials.
methodsWe applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases.
resultsOur best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. DISCUSSION: The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. HIGHLIGHTS: Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.
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