Voice recognition as an innovative method to identify and monitor heart failure - a review

Abstract

Screening of heart failure (HF) remains suboptimal. However, vocal biomarkers are an emerging tool to screen for HF and predict adverse outcomes. This article is a literature review of the current evidence, exploring the use of vocal biomarkers to screen for HF and predict hospitalisation and mortality. Voice may be utilised as a time-efficient method for the screening of HF, risk stratification, and monitoring of disease progression. Vocal biomarkers such as pause ratio and artificial neural networks can screen for HF accurately in a case-control setting. Other vocal markers like maximum phonation time, creak percent are useful to monitor disease progression. Vocal biomarkers appear to match current predictors of one-year mortality, HF related decompensation and hospitalisation. The integration of vocal biomarkers assessment in HF healthcare is promising across primary and secondary care settings. Key challenges including health data protection, regulatory compliance, and user acceptance must be addressed before these tools can be fully integrated into HF clinical care pathway.