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Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic review

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Number of pages56
JournalJournal of Alzheimer's Disease
Early online date12 Nov 2020
DOIs
Publication statusE-pub ahead of print - 12 Nov 2020

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 summarise 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.
Method: 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 summarise 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 standardisation, 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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