Cardiovascular

Arrhythmias

Latest AI and machine learning research in arrhythmias 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...

Bioimpedance assessment method based on back propagation neural network for irreversible electroporation of liver tissue.

The safety and efficacy of irreversible electroporation (IRE) in tumor therapy has been validated ov...

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 ...

Predicting early recurrence of hepatocellular carcinoma after thermal ablation based on longitudinal MRI with a deep learning approach.

BACKGROUND: Accurate prediction of early recurrence (ER) is essential to improve the prognosis of pa...

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...

[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...

[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...

A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration.

INTRODUCTION: Medical device recalls are important to the practice of emergency medicine, as unsafe ...

Regression study on fruit-setting days of purple eggplant fruit based on in situ VIS-NIRS and attention cycle neural network.

In the intelligent harvesting of eggplant, the lack of in situ identification technology makes it ch...

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...

Stratification of Early Arrhythmic Risk in Patients Admitted for Acute Coronary Syndrome: The Role of the Machine Learning-Derived "PRAISE Score".

BACKGROUND: The PRAISE (PRedicting with Artificial Intelligence riSk aftEr acute coronary syndrome) ...

Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity.

Artificial intelligence (AI)-enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can ...

Leveraging machine learning for preoperative prediction of supramaximal ablation in laser interstitial thermal therapy for brain tumors.

OBJECTIVE: Maximizing safe resection in neuro-oncology has become paramount to improving patient sur...

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