AIMC Topic: Myocardial Infarction
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Comprehensive electrocardiographic diagnosis based on deep learning.
Artificial intelligence in medicine
Jan 20, 2020
Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irre...
Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 3, 2020
BACKGROUND AND OBJECTIVE: Cardiac magnetic resonance imaging (MRI) can assist in both functional and structural analysis of the heart, but due to hardware and physical limitations, high-resolution MRI scans is time consuming and peak signal-to-noise ...
Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death.
Journal of biomedical semantics
Nov 12, 2019
BACKGROUND: Free text in electronic health records (EHR) may contain additional phenotypic information beyond structured (coded) information. For major health events - heart attack and death - there is a lack of studies evaluating the extent to which...
Evaluation of Machine Learning Algorithms for Predicting Readmission After Acute Myocardial Infarction Using Routinely Collected Clinical Data.
The Canadian journal of cardiology
Oct 25, 2019
BACKGROUND: The ability to predict readmission accurately after hospitalization for acute myocardial infarction (AMI) is limited in current statistical models. Machine-learning (ML) methods have shown improved predictive ability in various clinical c...
ML-ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG.
Computer methods and programs in biomedicine
Oct 17, 2019
BACKGROUND AND OBJECTIVE: Myocardial infarction (MI) is one of the most threatening cardiovascular diseases for human beings, which can be diagnosed by electrocardiogram (ECG). Automated detection methods based on ECG focus on extracting handcrafted ...
Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning.
Medical image analysis
Oct 4, 2019
Accurate and simultaneous segmentation and full quantification (all indices are required in a clinical assessment) of the myocardial infarction (MI) area are crucial for early diagnosis and surgical planning. Current clinical methods remain subject t...
A deep survival analysis method based on ranking.
Artificial intelligence in medicine
Jun 6, 2019
Survival analyses of populations and the establishment of prognoses for individual patients are important activities in the practice of medicine. Standard survival models, such as the Cox proportional hazards model, require extensive feature engineer...
A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.
JACC. Cardiovascular imaging
May 15, 2019
OBJECTIVES: This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional ...
Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI.
Radiology
Apr 30, 2019
Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for...
Fuzzy DEA-based classifier and its applications in healthcare management.
Health care management science
Mar 8, 2019
Nonlinear fuzzy classification models have better classification performance than linear fuzzy classifiers. In many nonlinear fuzzy classification problems, piecewise-linear fuzzy discriminant functions can approximate nonlinear fuzzy discriminant fu...
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