Cardiovascular

Myocardial Infarction

Latest AI and machine learning research in myocardial infarction for healthcare professionals.

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Are Wearable ECG Devices Ready for Hospital at Home Application?

The increasing focus on improving care for high-cost patients has highlighted the potential of Hospi...

Eliciting the Impact of Metformin and Statins on Prostate Cancer Outcomes from a Real-life National Database Analysis.

Several large analyses have revealed contradictory results regarding the association between prostat...

A Comprehensive Literature Review Discussing Diagnostic Challenges of Prinzmetal or Vasospastic Angina.

This narrative review addresses the diagnostic complexities of vasospastic angina (VSA), also known ...

Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.

BACKGROUND AND AIMS: Current heart failure (HF) risk stratification strategies require comprehensive...

Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study.

BACKGROUND AND AIMS: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) prov...

Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.

BACKGROUND AND AIMS: Robust and convenient risk stratification of patients with paediatric and adult...

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those wi...

[Research on arrhythmia classification algorithm based on adaptive multi-feature fusion network].

Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly i...

[The joint analysis of heart health and mental health based on continual learning].

Cardiovascular diseases and psychological disorders represent two major threats to human physical an...

Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.

AIMS: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) b...

[Application Status of Machine Learning in Assisted Diagnosis Techniques of Cardiovascular Diseases].

In recent years, cardiovascular disease has become a common disease. With the development of machine...

Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features.

Cardiovascular diseases (CVDs) have surpassed cancer and become the major cause of death worldwide. ...

Automatic quantification of left atrium volume for cardiac rhythm analysis leveraging 3D residual UNet for time-varying segmentation of ECG-gated CT.

Atrial fibrillation (AF) is a heart condition widely recognized as a significant risk factor for str...

Transfer learning in ECG diagnosis: Is it effective?

The adoption of deep learning in ECG diagnosis is often hindered by the scarcity of large, well-labe...

Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps.

OBJECTIVE: Evaluate popular explanation methods using heatmap visualizations to explain the predicti...

A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.

Recent endeavors have led to the exploration of Machine Learning (ML) to enhance the detection and a...

[A novel approach for assessing quality of electrocardiogram signal by integrating multi-scale temporal features].

During long-term electrocardiogram (ECG) monitoring, various types of noise inevitably become mixed ...

Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores.

BACKGROUND AND AIMS: Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach fo...

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