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      Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

      research-article
      a , * , b , a
      Journal of Alzheimer's Disease
      IOS Press
      Alzheimer’s disease, artificial intelligence, cognitive decline, computational linguistics, dementia, machine learning, screening, speech processing

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          Abstract

          Background:

          Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis.

          Objective:

          Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them.

          Methods:

          Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019.

          Results:

          From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations).

          Conclusion:

          Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

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          Most cited references115

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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            The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

            To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. Validation study. A community clinic and an academic center. Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC). The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
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              "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician.

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                Author and article information

                Journal
                J Alzheimers Dis
                J Alzheimers Dis
                JAD
                Journal of Alzheimer's Disease
                IOS Press (Nieuwe Hemweg 6B, 1013 BG Amsterdam, The Netherlands )
                1387-2877
                1875-8908
                06 November 2020
                08 December 2020
                2020
                : 78
                : 4
                : 1547-1574
                Affiliations
                [a ]Usher Institute, Edinburgh Medical School, The University of Edinburgh , Scotland, UK
                [b ]Centre for Clinical Brain Sciences, The University of Edinburgh , Scotland, UK
                Author notes
                [* ]Correspondence to: Sofia de la Fuente Garcia, Usher Institute, Edinburgh Medical School, The University of Edinburgh, Nine Edinburgh BioQuarter, 9 Little France Road, Edinburgh EH16 4UX, Scotland, UK. E-mail: sofia.delafuente@ 123456ed.ac.uk .
                Article
                JAD200888
                10.3233/JAD-200888
                7836050
                33185605
                7bc039cf-a25b-4b47-8c7d-bb0d28fea03f
                © 2020 – IOS Press and the authors. All rights reserved

                This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Research Article

                alzheimer’s disease,artificial intelligence,cognitive decline,computational linguistics,dementia,machine learning,screening,speech processing

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