A remote speech‐based AI system to screen for early Alzheimer's disease via smartphones

Fristed, Emil; Skirrow, Caroline; Meszaros, Marton; Lenain, Raphael; Meepegama, Udeepa; Cappa, Stefano F.; Aarsland, Dag; Weston, Jack

doi:10.1002/dad2.12366

Cited by 13 publications

(5 citation statements)

References 55 publications

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“…The tool is fully automated, partially addressing issues related to the frequent incongruence between the patient and examiner’s native language, and ultimately emphasizing its ease and accessibility in primary care and community health settings. Furthermore, the impact of the current model is a promising avenue to increase cognitive screening, as it could be easily adapted to mobile phones, the feasibility and efficacy of which has been demonstrated in prior studies ( 74 , 75 ). Thus, the current technology may allow for screening of more individuals that may be otherwise unable to access care.…”

Section: Discussionmentioning

confidence: 88%

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Kaser,

Lacritz,

Winiarski

et al. 2024

Front. Neurol.

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ObjectiveEarly detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.MethodData were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic “F” fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores.ResultsMean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively.ConclusionFindings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.

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Section: Discussionmentioning

confidence: 88%

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Kaser,

Lacritz,

Winiarski

et al. 2024

Front. Neurol.

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“…Fristed et al [ 139 ] developed a mobile app that made use of the Automatic Story Recall Task (ASRT), a remotely self-administered task about recalling a short story (that is, recorded speech analysis). Patients were obtained from the Amyloid Prediction in Early-Stage Alzheimer’s Disease From Acoustic and Linguistic Patterns of Speech (AMYPRED).…”

Section: Resultsmentioning

confidence: 99%

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Cabrera-León,

Báez,

Fernández-López

et al. 2024

Journal of Alzheimer’s Disease

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Background: The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer’s disease (AD), as the most common type of dementia, has become more frequent too. Background: Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future. Methods: Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected. Results: The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field. Conclusions: The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

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“…While these are promising, at present, literature mostly involves cross-sectional studies in AD. Examples are speech-based artificial intelligence (AI) applications predicting cognitive decline ( Fristed et al, 2022 ), biometric measures (e.g., skin conduction, pupillometry and eye-tracking patterns) reflecting social-emotional and/or linguistic deficits ( Mendez et al, 2018 ; Singleton et al, 2022 ; El Haj et al, 2024 ), AI-based imaging algorithms for longitudinal brain mapping ( Pérez-Millan et al, 2023 ), and proteomics technology detecting protein profiles ( Katzeff et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

The pursuit for markers of disease progression in behavioral variant frontotemporal dementia: a scoping review to optimize outcome measures for clinical trials

Fieldhouse,

van Paassen,

van Engelen

et al. 2024

Front. Aging Neurosci.

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Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative disorder characterized by diverse and prominent changes in behavior and personality. One of the greatest challenges in bvFTD is to capture, measure and predict its disease progression, due to clinical, pathological and genetic heterogeneity. Availability of reliable outcome measures is pivotal for future clinical trials and disease monitoring. Detection of change should be objective, clinically meaningful and easily assessed, preferably associated with a biological process. The purpose of this scoping review is to examine the status of longitudinal studies in bvFTD, evaluate current assessment tools and propose potential progression markers. A systematic literature search (in PubMed and Embase.com) was performed. Literature on disease trajectories and longitudinal validity of frequently-used measures was organized in five domains: global functioning, behavior, (social) cognition, neuroimaging and fluid biomarkers. Evaluating current longitudinal data, we propose an adaptive battery, combining a set of sensitive clinical, neuroimaging and fluid markers, adjusted for genetic and sporadic variants, for adequate detection of disease progression in bvFTD.

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A remote speech‐based AI system to screen for early Alzheimer's disease via smartphones

Cited by 13 publications

References 55 publications

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

The pursuit for markers of disease progression in behavioral variant frontotemporal dementia: a scoping review to optimize outcome measures for clinical trials

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