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Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review
de la Fuente Garcia S, Ritchie CW, Luz S.. Journal of Alzheimer's disease : JAD, 2020-01-01
Topic: Brain imaging, connectivity and assessment
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.
Key findings
- 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.
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Study details
- Study designsystematic review
- Evidence strengthHigh
How this study scored 69/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 40 of the 58 points we could judge it on, which is 69 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of Alzheimer's disease : JAD) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 12 / 12 | A systematic review of all the studies on the question |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | A review of other studies — randomisation is not credited here |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33185605) |
| Free to read | 2 / 2 | Free to read |
| Points | 40 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 40 of the 58 points we could judge it on is 69 out of 100. |
| Total | 69 / 100 | — |
What each of these 20 components measures, and what this score is not →
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APA 7th
de la Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020). Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review. Journal of Alzheimer's disease : JAD. https://doi.org/10.3233/jad-200888
Vancouver
de la Fuente Garcia S, Ritchie CW, Luz S. Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review. Journal of Alzheimer's disease : JAD. 2020. doi:10.3233/jad-200888. PMID: 33185605.
BibTeX
@article{delafuentegarcia2020-33185605,
author = {de la Fuente Garcia, S. and Ritchie, C. W. and Luz, S.},
title = {{Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review}},
journal = {Journal of Alzheimer's disease : JAD},
year = {2020},
doi = {10.3233/jad-200888},
pmid = {33185605},
url = {https://doi.org/10.3233/jad-200888}
}