View clinical trials related to Artificial Heart Device User.
Filter by:Artificial Intelligence, trained through model learning, can quickly perform medical image recognition and is widely used in early disease screening and assisted diagnosis. With the continuous optimization of deep learning, the application of AI has helped to discover some previously unknown associations with other systemic diseases. Artificial intelligence based on retinal fundus images can be used to detect anemia, hepatobiliary diseases, and chronic kidney disease, and to predict other systemic biomarkers. The above studies provide a theoretical basis for the application of artificial intelligence technology based on retinal fundus images to the diagnosis and prediction of cardiovascular diseases. At present, there is still a lack of accurate, rapid, and easy-to-use diagnostic and therapeutic tools for predictive modeling of coronary heart disease risk and early screening tools in China and the world. Fundus image is gradually used as a tool for extensive screening of diseases due to its special connection with blood vessels throughout the body, as well as easy access, cheap and efficient. It is of great scientific and social significance to develop and validate a model for identification and prediction of coronary heart disease and its risk factors based on fundus images using AI deep learning algorithms, and to explore the value of AI fundus images in assisting coronary heart disease diagnosis and screening for a wide range of applications.
Comparison of capillary whole blood INR determined by LumiraDx Instrument to venous plasma INR determined by laboratory reference method (IL ACL ELITE PRO) for method comparison and assessment of accuracy and bias by regression analysis and other analytical methods.
Left Ventricular Assist Device (LVAD) implantation is a common surgical procedure in patients with end-stage heart failure. Optimal fluid management is essential for adequate postoperative treatment. It is important to identify which patients will benefit from fluid administration. Passive leg raising (PLR) is a validated dynamic method to predict fluid responsiveness in patients with heart failure by inducing a transient increase in cardiac preload. Objective: To investigate the role of PLR on the management of patients following LVAD implantation in the postoperative period.
- Study Purpose: The purpose of this study is to evaluate the sensitivity/specificity of noninvasive diagnostic methods for the cardiac function that are based on data available from the Left Ventricular Assist Device (LVAD). These diagnostic methods will be compared to standard clinical diagnostic procedures both at rest and during exercise. - Study Design: The study is a prospective interventional cohort study. - Patient cohort: Thirty heart failure patients who had or are about to have a LVAD implanted. - Data Collection: The acquisition of LVAD data (motor current and speed) and wristwatch accelerometry data concurrently to the documentation of routine clinical examinations, such as cardiac ultrasound, electrocardiography (ECG), cardiac catheterization, and exercise tests, will be performed.