AIMC Topic: Cardiovascular Diseases

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Artificial intelligence age prediction using electrocardiogram data: Exploring biological age differences.

Heart rhythm
BACKGROUND: Biological age can be predicted using artificial intelligence (AI) trained on electrocardiograms (ECGs), which is prognostic for mortality and cardiovascular events.

Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand.

BMC medical informatics and decision making
BACKGROUND: In the age of big data, linked social and administrative health data in combination with machine learning (ML) is being increasingly used to improve prediction in chronic disease, e.g., cardiovascular diseases (CVD). In this study we aime...

Detection of cardiovascular disease cases using advanced tree-based machine learning algorithms.

Scientific reports
Cardiovascular disease (CVD) can often lead to serious consequences such as death or disability. This study aims to identify a tree-based machine learning method with the best performance criteria for the detection of CVD. This study analyzed data co...

Deep residual 2D convolutional neural network for cardiovascular disease classification.

Scientific reports
Cardiovascular disease (CVD) continues to be a major global health concern, underscoring the need for advancements in medical care. The use of electrocardiograms (ECGs) is crucial for diagnosing cardiac conditions. However, the reliance on profession...

Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization.

Scientific reports
To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing high...

Artificial intelligence in cardiology: a peek at the future and the role of ChatGPT in cardiology practice.

Journal of cardiovascular medicine (Hagerstown, Md.)
Artificial intelligence has increasingly become an integral part of our daily activities. ChatGPT, a natural language processing technology developed by OpenAI, is widely used in various industries, including healthcare. The application of ChatGPT in...

Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data.

Mechanisms of ageing and development
The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The main objective of the current study was to assess its relationship with other relevant biomarkers and ...

Application of AI-empowered scenario-based simulation teaching mode in cardiovascular disease education.

BMC medical education
BACKGROUND: Cardiovascular diseases present a significant challenge in clinical practice due to their sudden onset and rapid progression. The management of these conditions necessitates cardiologists to possess strong clinical reasoning and individua...

Image-based ECG analyzing deep-learning algorithm to predict biological age and mortality risks: interethnic validation.

Journal of cardiovascular medicine (Hagerstown, Md.)
BACKGROUND: Cardiovascular risk assessment is a critical component of healthcare, guiding preventive and therapeutic strategies. In this study, we developed and evaluated an image-based electrocardiogram (ECG) analyzing an artificial intelligence (AI...

Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models.

European journal of medical research
BACKGROUND: The incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction of cardiovascular disease is essential, and the use of machine learning and deep learning can aid in ...