Using Artificial Intelligence on Blood Pressure Sounds to Detect Heart Function
Tests whether sounds from blood pressure cuffs can flag weak heart pumping in adults.
Plain-language title generated from the registry record
Registry title: Artificial Intelligence in Detecting Cardiac Function
Key facts
- Where
- Yiwu, ChinaSee the site
Where in China
- The Fourth Hospital Affiliated to Zhejiang University School of MedicineYiwu, ChinaNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- The Fourth Affiliated Hospital of Zhejiang University School of Medicine
- Trial code
- NCT06444425
- Record updated
- 17 February 2025
- Original description
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.Read full original descriptionShow less
Korotkoff Sounds Dynamically Reflect Changes in Cardiac Function Based on Deep Learning Methods: A Prospective, Multicenter Study
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.
Inclusion: Patients who were admitted to the Cardiology Department of the aforementioned four Groups (The Fourth Affiliated Hospital of School of Medicine of Zhejiang University, Zhejiang Taizhou Hospital, Quzhou People\'s Hospital, Zhejiang Quhua Hospital)between June 2024 and December 2024, and successfully underwent echocardiographic examinations; Individuals aged between 18 and 90 years with a resting heart rate ranging from 60 to 100 beats per minute; Demonstrating good compliance, capable of cooperating in completing multiple blood pressure measurements within 72 hours following the completion of echocardiography; Willingness to voluntarily participate in the study.
Exclusion: Patients with acute decompensated heart failure, acute myocardial infarction, atrioventricular block; Cerebral hemorrhage, severe infection, active digestive tract ulcer, severe hematological diseases, severe liver and kidney dysfunction or other serious medical or surgical conditions; Patients with mental illness; Pregnant or lactating women; Individuals who have participated in other clinical trials within the past 3 months.