Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study.

Abstract

Background: The two most commonly used methods to identify frailty are the frailty phenotype and the frailty index. However, both methods have limitations in clinical application. In addition, methods for measuring frailty have not yet been standardized.

Objective: We aimed to develop and validate a classification model for predicting frailty status using vocal biomarkers in community-dwelling older adults, based on voice recordings obtained from the picture description task (PDT).

Methods: We recruited 127 participants aged 50 years and older and collected clinical information through a short form of the Comprehensive Geriatric Assessment scale. Voice recordings were collected with a tablet device during the Korean version of the PDT, and we preprocessed audio data to remove background noise before feature extraction. Three artificial intelligence (AI) models were developed for identifying frailty status: SpeechAI (using speech data only), DemoAI (using demographic data only), and DemoSpeechAI (combining both data types).

Results: Our models were trained and evaluated on the basis of 5-fold cross-validation for 127 participants and compared. The SpeechAI model, using deep learning-based acoustic features, outperformed in terms of accuracy and area under the receiver operating characteristic curve (AUC), 80.4% (95% CI 76.89%-83.91%) and 0.89 (95% CI 0.86-0.92), respectively, while the model using only demographics showed an accuracy of 67.96% (95% CI 67.63%-68.29%) and an AUC of 0.74 (95% CI 0.73-0.75). The SpeechAI model outperformed the model using only demographics significantly in AUC (t4=8.705 [2-sided]; P<.001). The DemoSpeechAI model, which combined demographics with deep learning-based acoustic features, showed superior performance (accuracy 85.6%, 95% CI 80.03%-91.17% and AUC 0.93, 95% CI 0.89-0.97), but there was no significant difference in AUC between the SpeechAI and DemoSpeechAI models (t4=1.057 [2-sided]; P=.35). Compared with models using traditional acoustic features from the openSMILE toolkit, the SpeechAI model demonstrated superior performance (AUC 0.89) over traditional methods (logistic regression: AUC 0.62; decision tree: AUC 0.57; random forest: AUC 0.66).

Conclusions: Our findings demonstrate that vocal biomarkers derived from deep learning-based acoustic features can be effectively used to predict frailty status in community-dwelling older adults. The SpeechAI model showed promising accuracy and AUC, outperforming models based solely on demographic data or traditional acoustic features. Furthermore, while the combined DemoSpeechAI model showed slightly improved performance over the SpeechAI model, the difference was not statistically significant. These results suggest that speech-based AI models offer a noninvasive, scalable method for frailty detection, potentially streamlining assessments in clinical and community settings.