2025-08-01 国立循環器病研究センター,株式会社太陽生命少子高齢社会研究所
(図1)認知機能障害予測モデルの概要
<関連情報>
- https://www.ncvc.go.jp/pr/release/pr_48425/
- https://www.thelancet.com/journals/lanwpc/article/PIIS2666-6065(25)00135-X/fulltext
コミュニティ居住成人における認知機能障害の検出を目的としたAIベースの音声バイオマーカーモデルの開発と検証:日本における横断的研究 Developing and testing AI-based voice biomarker models to detect cognitive impairment among community dwelling adults: a cross-sectional study in Japan
Eri Kiyoshige ∙ Soshiro Ogata ∙ Namhee Kwon ∙ Yuriko Nakaoku ∙ Chisato Hayashi ∙ Nate Blaylock ∙ et al.
The Lancet Regional Health – Western Pacific Published: June 12, 2025
DOI:https://doi.org/10.1016/j.lanwpc.2025.101598
Summary
Background
Voice is a potential biomarker of cognitive impairment because mild cognitive impairment (MCI) can cause changes in speech patterns and tempo. Artificial intelligence (AI) can deliver voice biomarkers as prediction features, leading to a timely, noninvasive, and cost-effective detection of cognitive impairment. This study aimed to develop and test prediction models utilizing voice biomarkers to detect cognitive impairment, which AI derived from voice data of unstructured conversations in community-dwelling adults in Japan.
Methods
This observational study with a cross-sectional design, included 1461 community-dwelling adults. The outcome was cognitive impairment assessed by the Memory Performance Index score from the MCI screen. Voice data was collected from 3-min open-question interviews and extracted voice biomarkers based on acoustic and prosodic features as a 512-dimensional vector of individual voice information using the voice generator, Wav2Vec2. Other considerable predictors were age, sex, and education. We developed cognitive impairment prediction models by applying the extreme gradient boosting decision tree algorithm and a deep neural network model using 979 participants. Prediction performances were tested by area under the curves (AUCs) in 482 participants who were not used for model development.
Findings
We had 967 women (66·2%), 526 cognitive impairment (36·0%) participants with mean (standard deviation) age and education years of 79·5 (6·3) years old and 11·6 (2·2) years, respectively. The inclusion of voice biomarkers significantly improved AUCs (95% confidence intervals), from 0·80 (0·76, 0·84) to 0·88 (0·84, 0·91) for the age sex model and from 0·78 (0·73, 0·82) to 0·89 (0·86, 0·92) for the age sex and education model (p < 0·0001 for both comparisons by DeLong test).
Interpretation
Our prediction models for cognitive impairment using voice biomarkers can provide significantly timesaving MCI screening with high prediction performances (AUC = 0·89). Voice biomarkers significantly contributed to improving prediction performance.
Funding
Small Business Innovation Research (SBIR Phase 3 Fund), the Intramural Research Fund of Cardiovascular Diseases of the National Cerebral and Cardiovascular Center, and JSPS KAKENHI.
