Cardiovascular

Congestive Heart Failure

Latest AI and machine learning research in congestive heart failure for healthcare professionals.

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Showing 421-441 of 3,374 articles
Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.

OBJECTIVES: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM...

Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study.

BACKGROUND: Echocardiography is the diagnostic modality for assessing cardiac systolic and diastolic...

A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.

AIMS: Noncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathoph...

A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging.

Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symp...

Left ventricular non-compaction cardiomyopathy automatic diagnosis using a deep learning approach.

BACKGROUND AND OBJECTIVE: Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy chara...

Intelligent Monitoring of Care Status for COPD Patients Based on Deep Learning.

To discuss the application method and effect of COPD patients in deep learning in intelligent monito...

The Role of Machine Learning in Cardiovascular Pathology.

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to ass...

MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data.

The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study t...

Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation.

Recently, deep learning approaches for MR motion artifact correction have been extensively studied. ...

Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma.

Diffuse gliomas are the most common malignant primary brain tumors. Identification of isocitrate deh...

Perioperative Outcomes of a Hydrocortisone Protocol after Endonasal Surgery for Pituitary Adenoma Resection.

 In pituitary adenomas (PAs), the use of postoperative steroid supplementation remains controversia...

Analyses of child cardiometabolic phenotype following assisted reproductive technologies using a pragmatic trial emulation approach.

Assisted reproductive technologies (ART) are increasingly used, however little is known about the lo...

Long-term effect of tocilizumab on left ventricular hypertrophy and systolic dysfunction in AA amyloidosis with rheumatoid arthritis.

Because cardiac involvement of amyloid A (AA) is not frequent, little is known about the effects of ...

Robustifying Deep Networks for Medical Image Segmentation.

The purpose of this study is to investigate the robustness of a commonly used convolutional neural n...

A machine learning-based biological aging prediction and its associations with healthy lifestyles: the Dongfeng-Tongji cohort.

This study aims to establish a biological age (BA) predictor and to investigate the roles of lifesty...

Multi-model fusion of classifiers for blood pressure estimation.

Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Co...

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