Artificial Intelligence in Detecting Cardiac Function

Condition(s) studied

Study summary

The Korotkoff Sounds（KS）, which have been in use for over a century, are widely regarded as the gold standard for measuring blood pressure. Furthermore, their potential extends beyond diagnosis and treatment of cardiovascular disease; however, research on the KS remains limited. Given the increasing incidence of heart failure (HF), there is a pressing need for a rapid and convenient prehospital screening method. In this study, we propose employing deep learning (DL) techniques to explore the feasibility of utilizing KS methodology in predicting functional changes in cardiac ejection fraction (LVEF) as an indicator of cardiac dysfunction.

Eligibility

Primary outcome measure(s)

• Cardiac Dysfunction — Evaluation LVEF：Cardiac ultrasound was performed 72 hours prior to and following anti-Acute Heart Failure treatment.
  The pathophysiology of heart failure encompasses a multifaceted clinical syndrome: 1)Cardiac structural and/or functional abnormalities resulting in impaired ventricular diastolic function and/or systolic ejection capacity;2)Corresponding clinical manifestations indicative of heart failure; 3)Typically accompanied by elevated levels of beneficial natriuretic peptide(BNP), as well as pulmonary or systemic congestion suggestive of cardiac origin on imaging studies, or objective evidence of increased ventricular filling pressure on hemodynamic assessments.4)According to the classification of left ventricular ejection fraction (LVEF) based on cardiac ultrasound, heart failure can be categorized into four types: reduced ejection fraction heart failure (HFrEF), improving ejection fraction heart failure (HFimpEF), moderately reduced ejection fraction heart failure (HFmrEF), and retained ejection fraction heart failure (HFpEF).

Trial sites (1)

More The Fourth Affiliated Hospital of Zhejiang University School of Medicine trials in China

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