A systematic literature review of automatic Alzheimer’s disease detection from speech and language

Abstract

Objective In recent years numerous studies have achieved promising results in Alzheimer’s Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. Materials and Methods We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? Results and Discussion We identified 33 eligible studies and 5 main findings: participants’ demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. Conclusion The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.

Towards early prediction of Alzheimer's disease through language samples

“…applied state of the art analysis procedures to samples from 270 participants finding that dementia onset before age 85 could be identified with 70–75% accuracy. This is well above chance performance, and slightly below the best performance seen in studies comparing current dementia patients with controls [6] . The authors then demonstrate that their model picks up on many of the same linguistic trends seen in previous comparisons of AD vs. controls.…”

Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech

“…These models serve as quick, objective, and non-invasive assessments of an individual's cognitive status which could be developed into more accessible tools to facilitate clinical screening and diagnosis. Since these initial reports, there has been a proliferation of studies reporting classification models for AD based on speech, as described by recent reviews and meta-analyses (Slegers et al, 2018;de la Fuente Garcia et al, 2020;Petti et al, 2020;Pulido et al, 2020), but the field still lacks validation of predictive models on publicly-available, balanced, and standardized benchmark datasets.…”

“…The existing studies that have addressed differences between AD and non-AD speech and worked on developing speech-based AD biomarkers, are often descriptive rather than predictive. Thus, they often overlook common biases in evaluations of AD detection methods, such as repeated occurrences of speech from the same participant, variations in audio quality of speech samples, and imbalances of gender and age distribution in the used datasets, as noted in the systematic reviews and metaanalyses published on this topic (Slegers et al, 2018;Chen et al, 2020;Petti et al, 2020). As such, the existing ML models may be prone to the biases introduced in available data.…”

Performance on the Picture Description Tasks in Patients with Late Dementia of the Alzheimer’s Type

“…In literature, there are also several studies related to informative language and speech features and they reveal that these features capture problems with word retrieval, semantic processing, acoustic impairment, and errors in speech and communication (Petti, Baker & Korhonen, 2020). However, syntactic ability is another crucial subject that gives information about the language ability of people with AD.…”