AIMC Topic: Heart Diseases

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Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction ...

A novel training method to preserve generalization of RBPNN classifiers applied to ECG signals diagnosis.

Neural networks : the official journal of the International Neural Network Society
In this paper a novel training technique is proposed to offer an efficient solution for neural network training in non-trivial and critical applications such as the diagnosis of health threatening illness. The presented technique aims to enhance the ...

Automation, machine learning, and artificial intelligence in echocardiography: A brave new world.

Echocardiography (Mount Kisco, N.Y.)
Automation, machine learning, and artificial intelligence (AI) are changing the landscape of echocardiography providing complimentary tools to physicians to enhance patient care. Multiple vendor software programs have incorporated automation to impro...

Granulomatosis with polyangiitis in Northeastern Brazil: study of 25 cases and review of the literature.

Advances in rheumatology (London, England)
BACKGROUND: Little has been published about the epidemiology of Granulomatosis with polyangiitis (GPA) in South America, especially in the intertropical zone, and no epidemiological data from Brazil are available. The purpose of the present study was...

Heart disease diagnosis based on mediative fuzzy logic.

Artificial intelligence in medicine
Mediative fuzzy logic is an approach able to deal with inconsistent information providing a solution when contradiction exists. The aim of this paper is to design an expert system based on this type of fuzzy logic in order to diagnose a possible hear...

Prediction of radiographic abnormalities by the use of bag-of-features and convolutional neural networks.

Veterinary journal (London, England : 1997)
This study evaluated the feasibility of bag-of-features (BOF) and convolutional neural networks (CNN) for computer-aided detection in distinguishing normal from abnormal radiographic findings. Computed thoracic radiographs of dogs were collected. For...

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

IEEE transactions on medical imaging
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been th...

Segmentation of histological images and fibrosis identification with a convolutional neural network.

Computers in biology and medicine
Segmentation of histological images is one of the most crucial tasks for many biomedical analyses involving quantification of certain tissue types, such as fibrosis via Masson's trichrome staining. However, challenges are posed by the high variabilit...

Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: Implications for natural language processing tools.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: Reporting standards promote clarity and consistency of stress myocardial perfusion imaging (MPI) reports, but do not require an assessment of post-test risk. Natural Language Processing (NLP) tools could potentially help estimate this ris...