AIMC Topic: Cardiovascular Diseases

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Application of Artificial Intelligence in Cardiovascular Medicine.

Comprehensive Physiology
The advent of advances in machine learning (ML)-based techniques has popularized wide applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In recent years, there has been a surge in the application of AI t...

Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology.

The Canadian journal of cardiology
Many clinicians remain wary of machine learning because of longstanding concerns about "black box" models. "Black box" is shorthand for models that are sufficiently complex that they are not straightforwardly interpretable to humans. Lack of interpre...

Stratifying the Risk of Cardiovascular Disease in Obstructive Sleep Apnea Using Machine Learning.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: Obstructive sleep apnea (OSA) is associated with higher risk of morbidity and mortality related to cardiovascular disease (CVD). Due to overlapping clinical risk factors, identifying high-risk patients with OSA who are likely t...

A Deep Learning Approach to Predict Diabetes' Cardiovascular Complications From Administrative Claims.

IEEE journal of biomedical and health informatics
People with diabetes require lifelong access to healthcare services to delay the onset of complications. Their disease management processes generate great volumes of data across several domains, from clinical to administrative. Difficulties in access...

Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor.

Advanced materials (Deerfield Beach, Fla.)
Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low-cost, lightweight, and mechanically durable textile triboelectric sensor that can...

Deep neural network-estimated electrocardiographic age as a mortality predictor.

Nature communications
The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A d...

Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran.

Scientific reports
This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 year...

Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis.

Cardiovascular ultrasound
Ultrasound is one of the most important examinations for clinical diagnosis of cardiovascular diseases. The speed of image movements driven by the frequency of the beating heart is faster than that of other organs. This particularity of echocardiogra...

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.

Scientific reports
Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy ...