AIMC Topic: Cardiovascular Diseases
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Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.
Journal of medical systems
Jul 19, 2024
Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related s...
Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility.
Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
Jul 16, 2024
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatmen...
A machine learning algorithm for stratification of risk of cardiovascular disease in metabolic dysfunction-associated steatotic liver disease.
European journal of internal medicine
Jul 16, 2024
BACKGROUND: Steatotic liver disease (SLD) is associated with adverse cardiac events. Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as a condition characterized by the abnormal accumulation of hepatic lipids that is clos...
Development and validation of 10-year risk prediction models of cardiovascular disease in Chinese type 2 diabetes mellitus patients in primary care using interpretable machine learning-based methods.
Diabetes, obesity & metabolism
Jul 15, 2024
AIM: To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods.
Advancing Cardiovascular Health Equity With Artificial Intelligence: A Collective Ethical Responsibility.
Circulation
Jul 15, 2024
Effects of precise cardio sounds on the success rate of phonocardiography.
PloS one
Jul 15, 2024
This work investigates whether inclusion of the low-frequency components of heart sounds can increase the accuracy, sensitivity and specificity of diagnosis of cardiovascular disorders. We standardized the measurement method to minimize changes in si...
A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques.
ESC heart failure
Jul 11, 2024
AIMS: The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease.
Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications.
The Canadian journal of cardiology
Jul 9, 2024
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clini...
CPSS: Fusing consistency regularization and pseudo-labeling techniques for semi-supervised deep cardiovascular disease detection using all unlabeled electrocardiograms.
Computer methods and programs in biomedicine
Jul 4, 2024
BACKGROUND AND OBJECTIVE: Deep learning usually achieves good performance in the supervised way, which requires a large amount of labeled data. However, manual labeling of electrocardiograms (ECGs) is laborious that requires much medical knowledge. S...
Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using machine learning-assisted QSAR modeling and virtual reverse pharmacology approach.
Molecular diversity
Jul 2, 2024
Cardiovascular disease is a chronic inflammatory disease with high mortality rates. TNF-alpha is pro-inflammatory and associated with the disease, but current medications have adverse effects. Therefore, efficient inhibitors are urgently needed as al...
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