Age Ageing. 2025 Aug 29;54(10):afaf316. doi: 10.1093/ageing/afaf316.
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.
PMID:41148189 | DOI:10.1093/ageing/afaf316
