Development of Thai Picture Description Task for Alzheimer’s Screening using Part-of-Speech Tagging

Sangchocanonta, Sirikorn; Vongsurakrai, S.; Sroykhumpa, K; Ellermann, V; Munthuli, Adirek; Anansiripinyo, Thanaporn; Onsuwan, Chutamanee; Hemrungrojn, Solaphat; Kosawat, Krit; Tantibundhit, Charturong

doi:10.1109/embc46164.2021.9629861

Cited by 2 publications

(7 citation statements)

References 14 publications

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“…Studies have shown that AI assessments, 154 in particular, those developed based on individuals' 155 textual 116,120,123,152,153,156 or vocal samples 13,86,87,135,136,144,151,157,158 could potentially be integrated into dementia care settings. 10,14,159 This can help clinicians identify linguistic and acoustic impairments associated with the initial stages of dementia, 160,161 MCI, 162,163 and AD.…”

Section: Discussionmentioning

confidence: 99%

“…Ratio of repetitions (RR) 17,116 ; Ratio of self-corrections (RC) Lexical Repetition AD; Dementia Number of utterances per individuals (No. U/I) 117 ; Number of verbs per utterance (No.…”

Section: Features Name and Refrences Category Associated Withmentioning

confidence: 99%

“…Various ML and DL algorithms have been proposed to develop AI-powered speech and language assessments. Examples of ML algorithms including k-nearest neighbor, 105,109,118 support vector machines (SVMs), ,86,96,105,109,145 multi-layer perceptron, 86,116,[147][148][149] and logistic regression. 17,135,137,147,150 Various DL algorithms, such as convolutional neural network (CNN), 86,116,145,148,149 long short-term memory, [151][152][153] RNN, 86 and BERT have also been employed to develop AI systems for dementia.…”

Section: Machine Learning (Ml) and Deep Learning (Dl) As The Basis Of...mentioning

confidence: 99%

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AI‐based assessments of speech and language impairments in dementia

Parsapoor

2023

Alzheimer's & Dementia

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Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI‐powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.

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Section: Discussionmentioning

confidence: 99%

“…Ratio of repetitions (RR) 17,116 ; Ratio of self-corrections (RC) Lexical Repetition AD; Dementia Number of utterances per individuals (No. U/I) 117 ; Number of verbs per utterance (No.…”

Section: Features Name and Refrences Category Associated Withmentioning

confidence: 99%

Section: Machine Learning (Ml) and Deep Learning (Dl) As The Basis Of...mentioning

confidence: 99%

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AI‐based assessments of speech and language impairments in dementia

Parsapoor

2023

Alzheimer's & Dementia

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“…Sangchocanonta et al [53] implemented MLP to classify between MCI, AD, and HC. The authors developed two new picture description tasks and transcribed the speech automatically.…”

Section: Deep Learningmentioning

confidence: 99%

“…Part of speech tagger (POS) is performed by Sangchocanonta et al [53] to group words into seven different categories. POS tool is used in natural language processing to group words into their grammatical categories (e.g., verb, noun, etc.)…”

Section: Linguistic Featuresmentioning

confidence: 99%

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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Development of Thai Picture Description Task for Alzheimer’s Screening using Part-of-Speech Tagging

Cited by 2 publications

References 14 publications

AI‐based assessments of speech and language impairments in dementia

AI‐based assessments of speech and language impairments in dementia

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

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