Key result
Deep learning analysis of Korotkoff sounds offers a novel approach to predict heart failure onset.
Why the study?
Increasing incidences of HF necessitate a rapid and convenient pre-hospital screening method, but the efficacy of K-sounds for diagnosing and treating cardiovascular diseases remains limited.
Deep learning analysis of Korotkoff sounds is proposed as a potential rapid, non-invasive pre-hospital screening tool for detecting heart failure and cardiac dysfunction.
May support Korotkoff-based deep learning for pre-hospital HF screening; leaves open prospective validation before clinical adoption.
Korotkoff sounds (K-sounds) have been around for over 100 years and are considered the gold standard for blood pressure (BP) measurement. K-sounds are also unique for the diagnosis and treatment of cardiovascular diseases; however, their efficacy is limited. The incidences of heart failure (HF) are increasing, which necessitate the development of a rapid and convenient pre-hospital screening method. In this review, we propose a deep learning (DL) method and the possibility of using K-methods to predict cardiac function changes for the detection of cardiac dysfunctions.
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Lin et al. (2022) studied heart failure. Korotkoff sounds was evaluated on Predictive model for cardiac function based on Korotkoff sounds dynamics. Korotkoff sounds can dynamically reflect changes in cardiac function, providing a novel approach to predict heart failure onset using deep learning methods.
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