Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review.
Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review.
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DOI：
10.3233/jad-200888
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Luz S
中科院分区：
文献类型：
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作者：
de la Fuente Garcia S;Ritchie CW;Luz S
语言是阿尔茨海默病临床信息的宝贵来源，因为它与神经变性同时下降。因此，语音和语言数据已被广泛研究与它的诊断。首先，总结人工智能、语音和语言处理在阿尔茨海默病背景下预测认知能力下降方面的现有发现。其次，详细说明当前的研究程序，突出其局限性，并提出解决这些问题的策略。对2000年至2019年的原始研究进行系统评价，注册在PROSPERO（参考文献CRD42018116606）。一个跨学科的搜索涵盖了六个数据库：工程学（ACM和IEEE）、心理学（PsycINFO）、医学（PubMed和Embase）和Web of Science。相关论文的文献筛选截止到2019年12月。从3,654个搜索结果中，根据资格标准选择了51篇文章。四个表格总结了他们的发现：研究细节（目的、人群、干预措施、比较、方法和结果）、数据细节（规模、类型、模式、注释、平衡、可用性和研究语言）、方法（预处理、特征生成、机器学习、评估和结果）和临床适用性（研究意义、临床潜力、偏倚风险和优势/局限性）。几乎所有51项研究都报告了有希望的结果，但很少在临床研究或实践中实施。该领域的主要局限性是标准化程度差，结果可比性有限，研究目标和临床应用之间存在一定程度的脱节。积极尝试缩小这些差距将有助于将未来的研究转化为临床实践。
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. 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. 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. 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). 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.