ArticleMethods in molecular biology (Clifton, N.J.)2024
Speech-Based Digital Biomarkers for Alzheimer's Research.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 citations in OpenAlex.
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- From "invisible" to "audible": Features extracted during simple speech tasks classify patient-reported fatigue in multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital biomarkers are of growing interest in the field of Alzheimer's Disease (AD) research. Digital biomarker data arising from digital health tools holds various potential benefits: more objective and more accurate assessment of patients' symptoms and remote collection of signals in real-world scenarios but also multimodal variance for prediction models of individual disease progression. Speech can be collected at minimal patient burden and provides rich data for assessing multiple aspects of AD pathology including cognition. However, the operations around collecting, preparing, and validly interpreting speech data within the context of clinical research on AD remains complex and sometimes challenging. Through a dedicated pipeline of speech collection tools, preprocessing steps and algorithms, precise qualification and quantification of an AD patient's pathology can be achieved from their speech. The aim of this chapter is to describe the methods that are needed to create speech collection scenarios that result in valuable speech-based digital biomarkers for clinical research.
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Registered trials
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