Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts

Schäfer, Simona; Mallick, Elisa; Schwed, Louisa; König, Alexandra; Zhao, Jinbin; Linz, Nicklas; Bodin, Timothy Hadarsson; Skoog, Johan; Possemis, Nina; Huurne, Daphne ter; Zettergren, Anna; Kern, Silke; Sacuiu, Simona; Ramakers, Inez H.G.B.; Skoog, Ingmar; Tröger, J.

doi:10.3233/jad-220762

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…It is worth noting that most of the examined studies predominantly focused on single-language datasets, and only one study adopted a multicultural and multilingual [79] database for dementia detection from speech. Moreover, only two studies used two distinct languages [75,76] for dementia detection from speech. While these studies have undoubtedly contributed valuable insights into the application of machine learning in speech detection, the variability in the languages of the data is one of the challenges that could be explored further to build a system that can generalize to multiple languages.…”

Section: Discussionmentioning

confidence: 99%

“…Schäfer et al [76] extracted over 50 features from two neuropsychological assessments, Rey-Audi-tory Verbal Learning Test (RAVLT) and Semantic Verbal Fluency, and 27 features were selected for analysis. The study grouped the extracted features into three neurocognitive subdomains, encompassing learning and memory, executive function, and processing speed, and calculated a single global composite score from these subdomains to represent an overall measure of cognitive function based on speech patterns.…”

Section: Feature Characteristicsmentioning

confidence: 99%

“…Automatic transcription was performed with the help of Kaldi software [51,52]. Schäfer et al [76] automatically process speech using a proprietary speech analysis pipeline. This pipeline includes automatic speech recognition for transcription and feature extraction, allowing them to analyze the verbal output of participants.…”

Section: Speech Analysis Approachmentioning

confidence: 99%

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Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review

Al-Hammadi,

Fleyeh,

Åberg

et al. 2024

JAD

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Background: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer’s disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection. Objective: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods. Methods: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review. Results: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results. Conclusions: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.

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Section: Discussionmentioning

confidence: 99%

Section: Feature Characteristicsmentioning

confidence: 99%

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Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review

Al-Hammadi,

Fleyeh,

Åberg

et al. 2024

JAD

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“…Die Anwendung von KI-basierter Sprachanalyse in der Neurologie bietet zahlreiche Vorteile, darunter möglicherweise eine frühere und präzisere Diagnose für Erkrankungen, für die es keine geeigneten Biomarker gibt. Sie ist in der Lage, feine Veränderungen in der Sprache zu erfassen, die auf neurologische Störungen hinweisen, und ermöglicht eine potenzielle Früherkennung [3,5,15,17,29]. gelnder Effektivität, des hohen zeitlichen Aufwands, der mangelnden Benutzerfreundlichkeit und der hohen Kosten kritisiert [7].…”

Section: Ki-basierte Sprachanalyse Für Neurologische Erkrankungenunclassified

Einsatz KI-gestützter Diagnostik von Sprach- und Sprechstörungen bei neurodegenerativen Erkrankungen

Fischer,

Jäck

2023

Nervenheilkunde

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ZUSAMMENFASSUNG Gegenstand und Ziel Der Artikel behandelt die Anwendungsmöglichkeiten der KI-gestützten Sprachanalyse bei neurodegenerativen Erkrankungen. Das Ziel besteht darin, einen Überblick über die sprachlichen Auffälligkeiten bei verschiedenen Krankheiten zu geben und zu zeigen, wie KI-basierte Methoden zur Diagnosestellung und Behandlung eingesetzt werden können. Material und Methoden Es werden neurodegenerative Erkrankungen und ihre spezifischen sprachlichen Störungen vorgestellt. Die traditionellen Methoden der Sprachanalyse für neurologische Erkrankungen werden erläutert und Möglichkeiten der KI-gestützten Analyse diskutiert. Ergebnisse Die KI-basierte Sprachanalyse stellt eine vielversprechende Möglichkeit zur Früherkennung und Diagnosestellung von neurologischen Erkrankungen dar. Durch automatische Transkripte und Fehleranalysen können subtile Veränderungen der Sprache und des Sprechens erkannt und objektiviert werden. Die KI-basierte Sprachanalyse ermöglicht eine genaue und quantifizierbare Bewertung von sprachlichen Defiziten und kann Fachpersonal zusätzliche Informationen liefern. Schlussfolgerung Die KI-basierte Sprachanalyse bietet neue Möglichkeiten zur Früherkennung und Überwachung von neurologischen Erkrankungen. Sie kann subtile Veränderungen der Sprache frühzeitig erkennen und eine rechtzeitige Intervention ermöglichen. Dennoch sollte sie als unterstützendes Werkzeug betrachtet werden und nicht als Ersatz für die Expertise von Fachpersonal. Die KI-basierte Sprachanalyse kann Ressourcen schonen, die Genauigkeit der Diagnose verbessern und eine kontinuierliche Verlaufsdiagnostik ermöglichen. Klinische Relevanz Die KI-basierte Sprachanalyse kann dazu beitragen, neurodegenerative Erkrankungen frühzeitig zu erkennen und eine gezielte Behandlung einzuleiten. Sie bietet eine objektivierbare Methode zur Bewertung sprachlicher Defizite und kann die Diagnosestellung unterstützen.

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“…Some alternative approaches offer greater levels of flexibility and availability. For instance, screening based on speech patterns [ 26 , 27 ] and mobile applications [ 28 , 29 ] leverages digital versions of widely used cognitive tests.…”

Section: Introductionmentioning

confidence: 99%

Detection of Alzheimer’s Disease Using Logistic Regression and Clock Drawing Errors

Lazarova,

Grigorova,

Petrova-Antonova

2023

Brain Sciences

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Alzheimer’s disease is an incurable disorder that accounts for up to 70% of all dementia cases. While the prevalence of Alzheimer’s disease and other types of dementia has increased by more than 160% in the last 30 years, the rates of undetected cases remain critically high. The present work aims to address the underdetection of Alzheimer’s disease by proposing four logistic regression models that can be used as a foundation for community-based screening tools that do not require the participation of medical professionals. Our models make use of individual clock drawing errors as well as complementary patient data that is highly available and easily collectible. All models were controlled for age, education, and gender. The discriminative ability of the models was evaluated by area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow test, and calibration plots were used to assess calibration. Finally, decision curve analysis was used to quantify clinical utility. We found that among 10 possible CDT errors, only 3 were informative for the detection of Alzheimer’s disease. Our base regression model, containing only control variables and clock drawing errors, produced an AUC of 0.825. The other three models were built as extensions of the base model with the step-wise addition of three groups of complementary data, namely cognitive features (semantic fluency score), genetic predisposition (family history of dementia), and cardio-vascular features (BMI, blood pressure). The addition of verbal fluency scores significantly improved the AUC compared to the base model (0.91 AUC). However, further additions did not make a notable difference in discriminatory power. All models showed good calibration. In terms of clinical utility, the derived models scored similarly and greatly outperformed the base model. Our results suggest that the combination of clock symmetry and clock time errors plus verbal fluency scores may be a suitable candidate for developing accessible screening tools for Alzheimer’s disease. However, future work should validate our findings in larger and more diverse datasets.

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Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts

Cited by 7 publications

References 21 publications

Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review

Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review

Einsatz KI-gestützter Diagnostik von Sprach- und Sprechstörungen bei neurodegenerativen Erkrankungen

Detection of Alzheimer’s Disease Using Logistic Regression and Clock Drawing Errors

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