Diagnostic utility of speech-based biomarkers in mild cognitive impairment: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Among various tools developed for mild cognitive impairment (MCI) detection, analysing speech features is a non-invasive and cost-effective approach that shows promise for early detection. This review aimed to systematically synthesise and analyse current evidence on the diagnostic utility of speech-based biomarkers for identifying MCI. METHODS: A systematic review and meta-analysis were conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Scopus, Ovid Medline and PsycINFO databases were searched up to April 2025 without restrictions on language, article status or year. RESULTS: Of 4432 identified records, 54 peer-reviewed articles met the inclusion criteria. Fixed-effects meta-analyses showed pooled estimates of 80.0% 'accuracy' [95% confidence intervals (CI): 70.0%-89.0%, P < .001, n = 21], 78.0% 'area under the curve' (95% CI: 70.0%-86.0%, P < .001, n = 21), 80.0% 'sensitivity' (95% CI: 71.0%-90.0%, P < .001, n = 22), and 77.0% 'specificity' (95% CI: 65.0%-89.0%, P < .001, n = 15) in differentiating MCI from cognitively unimpaired (CU) individuals. Egger's regression tests indicated no publication bias (P ≥ .299), and the I2 statistic revealed no heterogeneity across studies (I2 = 0.00%, P = 1.00). Four studies also included a subjective cognitive decline group, reporting significant differences in certain speech features compared to CU. CONCLUSIONS: Speech analysis demonstrates moderate classification performance, with balanced sensitivity and specificity, in distinguishing MCI from CU, suggesting its potential as an accurate and cost-effective diagnostic tool for MCI detection. Further research is needed to address variations in study methodologies, refine speech analysis protocols and validate findings in diverse populations to enhance generalisability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".