ArticleFrontiers in human neuroscience2021
Classification of Alzheimer's Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Speech changes in old age: Methodological considerations for speech-based discrimination of healthy ageing and Alzheimer's disease.International journal of language & communication disordersPooled it
- Eye-tracking-while-reading: A living survey of datasets with open library support.Behavior research methods · 2026Article
- Facial phenotypes in Alzheimer's disease: from neurobiology to artificial intelligence.Alzheimer's research & therapy · 2026Review
- The Utility of Speech and Language Analytics for Screening Alzheimer's Disease.Neuro-degenerative diseases · 2026Review
- Advances in ocular motor and pupil biomarkers for neurological disorders.Brain communications · 2026Review
- A systematic review of explainable artificial intelligence methods for speech-based cognitive decline detection.NPJ digital medicine · 2025Article
- 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
- Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review.NPJ digital medicine · 2025Article
- Age-related changes in connected speech production: evidence from eye-tracking in the culturally adapted picture description task.Frontiers in psychology · 2024Article
- Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.Current Alzheimer research · 2024Review
- Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review.Health information science and systems · 2023Article
- Delayed and More Variable Unimanual and Bimanual Finger Tapping in Alzheimer's Disease: Associations with Biomarkers and Applications for Classification.Journal of Alzheimer's disease : JAD · 2023Article
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Authors and funding
13 authors.
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Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discriminative ability for memory clinic patients. Fusing the multimodal data across tasks yielded the highest overall AUC of 0.83 ± 0.01, indicating that the data from novel tasks are complementary to established tasks.
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