Congestive Heart Failure

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, symptoms such as chest pain and dyspnea, and comorbidity related to cardiovascular diseases. Usually, these variables are analyzed by logistic regression to quantifying their relationship with the outcome; nevertheless, their predictive value is limited. In the present study, we aimed to investigate the...

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

BACKGROUND AND OBJECTIVE: Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy characterised by a thick and spongy left ventricle wall caused by the high presence of trabeculae (hyper-trabeculation). Recently, the percentage of the trabecular volume to the total volume of the external wall of the left ventricle (VT%) has been proposed to diagnose this illness.

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 monitoring, two groups were used under a reasonable sele...

The Role of Machine Learning in Cardiovascular Pathology.

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning t...

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 the combined impact of mitochondrial and nuclear-en...

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning.

This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict lo...

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

Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performa...

Application of Deep Neural Network Factor Analysis Model in Operating Room Management Nursing Analysis of Postoperative Infection Nursing after Thoracic Surgery.

Thoracic surgery is the main surgical method for the treatment of respiratory diseases and lung diseases, but infections caused by improper care are p...

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 dehydrogenase 1 (IDH1) mutations aids the diagnostic ...

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

 In pituitary adenomas (PAs), the use of postoperative steroid supplementation remains controversial, as it reduces peritumoral edema and sinonasal c...

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-...

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 long-term health of ART-conceived offspring. Weak se...

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 tocilizumab (TCZ; a humanized monoclonal anti-inte...

Robustifying Deep Networks for Medical Image Segmentation.

The purpose of this study is to investigate the robustness of a commonly used convolutional neural network for image segmentation with respect to near...

Artificial Intelligence (AI) approach to identifying factors that determine systolic blood pressure in type 2 diabetes (study from the LOOK AHEAD cohort).

BACKGROUND AND AIMS: Artificial Intelligence (AI) methods have recently become critical for research in diabetes in the era of big-data science.

Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm.

BACKGROUND: Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative ...

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 lifestyles on biological aging. The 14,848 participants w...

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 Commission. Hence, detecting prehypertension in time...

Vaso reactivity test using inhaled nitric oxide for pulmonary arterial hypertension accompanied by severe interstitial lung disease attributed to systemic sclerosis: A case report.

A 70-year-old man with severe interstitial pneumonia attributed to limited cutaneous systemic sclerosis was referred to our institution because of wor...

A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions.

OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of exper...