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Marginal Means Plots displaying the marginal means for all features with significant results from both sets of ANCOVAs (A–G) Unimpaired vs. Impaired, and (H) CUS vs. CUD. Marginal means are estimated using model parameters, holding Age, Education, Gender, and Unique Nodes constant.
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Clinical assessments often use complex picture description tasks to elicit natural speech patterns and magnify changes occurring in brain regions implicated in Alzheimer's disease and dementia. As The Cookie Theft picture description task is used in the largest Alzheimer's disease and dementia cohort studies available, we aimed to create algorithms...
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Introduction
Chronic kidney disease is related to neurodegeneration and structural changes in the brain which might lead to cognitive decline. The Fazekas scale used for assessing white matter hyperintensities (WMHs) was associated with poor cognitive performance. Therefore, this study investigated the associations between the mini-mental status ex...
Citations
... Second, the DementiaBank dataset, prepared by researchers at the University of Pittsburgh's Alzheimer Research Program, is a set of textual and vocal data samples obtained from older adults while they completed the picture description task, including the CTP description task. It has been a benchmark dataset for developing AI-powered speech and language assessments for dementia (42-44) and cognitive impairment detection (45). Thus, the Dementiabank dataset could be used as a pre-trained dataset. ...
Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transition from suicidal thoughts to suicide attempts. Advances in artificial intelligence have revolutionized the development of suicide screening tools and suicide risk detection systems. Thus, various types of AI systems, including text-based systems, have been proposed to identify individuals at risk of suicide. Although these systems have shown acceptable performance, most of them have not incorporated suicide theories in their design. Furthermore, directly applying suicide theories may be difficult because of the diversity and complexity of these theories. To address these challenges, we propose an approach to develop speech- and language-based suicide risk detection systems. We highlight the promise of establishing a benchmark textual and vocal dataset using a standardized speech and language assessment procedure, and research designs that distinguish between the risk factors for suicide attempt above and beyond those for suicidal ideation alone. The benchmark dataset could be used to develop trustworthy machine learning or deep learning-based suicide risk detection systems, ultimately constructing a foundation for vocal and textual-based suicide risk detection systems.
... Note some tasks performed either by healthy and dementia participants or only by dementia participants. Several studies [333,375,376,377,378] have used Pitt corpus to develop systems to detect dementia [333], to identify dementia [375], to diagnose AD [376], to detect cognitive impairment [377]. ...
... Note some tasks performed either by healthy and dementia participants or only by dementia participants. Several studies [333,375,376,377,378] have used Pitt corpus to develop systems to detect dementia [333], to identify dementia [375], to diagnose AD [376], to detect cognitive impairment [377]. ...
Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.
... Most of the above-mentioned SLAMs [14,15,20,27,30,37] have developed based on ML and deep learning (DL) algorithms [18,26,38] that have been trained using extracted linguistic and acoustic features from benchmark speech and language datasets. Examples of such datasets are DementiaBank (DB) dataset (i.e. the DB dataset is a collection of patients' speech and transcriptions) [39]; 2 Pitt corpus 3 [28,[40][41][42][43]; and Alzheimer's Dementia Recognition through Spontaneous Speech or ADReSS Challenge Dataset [28] that has been developed by modifying the Pitt corpus. For example, in [44] a SLAM has been developed using lexical features which have been extracted from the DB. ...
Objectives:
Automatic speech and language assessment methods (SLAMs) can help clinicians assess speech and language impairments associated with dementia in older adults. The basis of any automatic SLAMs is a machine learning (ML) classifier that is trained on participants' speech and language. However, language tasks, recording media, and modalities impact the performance of ML classifiers. Thus, this research has focused on evaluating the effects of the above-mentioned factors on the performance of ML classifiers that can be used for dementia assessment.
Methodology:
Our methodology includes the following steps: (1) Collecting speech and language datasets from patients and healthy controls; (2) Using feature engineering methods which include feature extraction methods to extract linguistic and acoustic features and feature selection methods to select most informative features; (3) Training different ML classifiers; and (4) Evaluating the performance of ML classifiers to investigate the impacts of language tasks, recording media, and modalities on dementia assessment.
Results:
Our results show that (1) the ML classifiers trained with the picture description language task perform better than the classifiers trained with the story recall language task; (2) the data obtained from phone-based recordings improves the performance of ML classifiers compared to data obtained from web-based recordings; and (3) the ML classifiers trained with acoustic features perform better than the classifiers trained with linguistic features.
Conclusion:
This research demonstrates that we can improve the performance of automatic SLAMs as dementia assessment methods if we: (1) Use the picture description task to obtain participants' speech; (2) Collect participants' voices via phone-based recordings; and (3) Train ML classifiers using only acoustic features. Our proposed methodology will help future researchers to investigate the impacts of different factors on the performance of ML classifiers for assessing dementia.
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.
