AIMC Topic: Heart Diseases

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Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study.

Cardiovascular diabetology Jan 29, 2021
BACKGROUND: We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term ris...
Deep Learning Time Factors Metabolic Syndrome Adipose Tissue Predictive Value of Tests Aged, 80 and over Male Middle Aged Risk Assessment Prevalence Adiposity Pericardium Cardiometabolic Risk Factors Radiographic Image Interpretation, Computer-Assisted Registries Non-alcoholic Fatty Liver Disease Los Angeles Heart Diseases Humans Prospective Studies Prognosis Tomography, X-Ray Computed Female Aged
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Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques.

Journal of healthcare engineering Jan 28, 2021
Heart angiography is a test in which the concerned medical specialist identifies the abnormality in heart vessels. This type of diagnosis takes a lot of time by the concerned physician. In our proposed method, we segmented the interested regions of h...
Algorithms Image Processing, Computer-Assisted Heart Heart Diseases Humans Machine Learning
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Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model.

BMC cardiovascular disorders Jan 18, 2021
BACKGROUND: Due to the limited number of studies with long term follow-up of patients undergoing Percutaneous Coronary Intervention (PCI), we investigated the occurrence of Major Adverse Cardiac and Cerebrovascular Events (MACCE) during 10 years of f...
Humans Machine Learning Percutaneous Coronary Intervention Coronary Artery Disease Male Iran Middle Aged Stents Heart Diseases Risk Assessment Cerebrovascular Disorders Proportional Hazards Models Time Factors Decision Support Techniques Treatment Outcome Retrospective Studies Female Risk Factors Aged
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Artificial Intelligence-Powered Measurement of Left Ventricular Ejection Fraction Using a Handheld Ultrasound Device.

Ultrasound in medicine & biology Jan 13, 2021
The aim of this study was to assess the accuracy of an algorithm for automated measurement of left ventricular ejection fraction (LVEF) available on handheld ultrasound devices (HUDs). One hundred twelve patients admitted to the cardiology department...
Middle Aged Humans Heart Ventricles Artificial Intelligence Heart Diseases Software Echocardiography, Three-Dimensional Female Aged Male Stroke Volume Echocardiography
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Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers.

Circulation. Arrhythmia and electrophysiology Jan 5, 2021
BACKGROUND: ECG interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare cardiac diseases. Deep neural networks (DNNs) can discover complex features in ECGs and may facilita...
Humans Electrocardiography DNA Mutation Heart Diseases Retrospective Studies DNA Mutational Analysis Female Calcium-Binding Proteins Adult Male Deep Learning
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Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network.

Circulation. Cardiovascular imaging Dec 15, 2020
BACKGROUND: Pathological atrial fibrosis is a major contributor to sustained atrial fibrillation. Currently, late gadolinium enhancement (LGE) scans provide the only noninvasive estimate of atrial fibrosis. However, widespread adoption of atrial LGE ...
Observer Variation Automation Organometallic Compounds Predictive Value of Tests Fibrosis Heart Atria Heart Diseases Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Reproducibility of Results Contrast Media Magnetic Resonance Imaging
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Early and accurate detection and diagnosis of heart disease using intelligent computational model.

Scientific reports Nov 12, 2020
Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the bod...
ROC Curve Computational Biology Follow-Up Studies Models, Statistical Aged Humans Heart Diseases Aged, 80 and over Artificial Intelligence Adult Machine Learning Male Prognosis Middle Aged Female
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A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF.

The international journal of cardiovascular imaging Oct 8, 2020
Left ventricular ejection fraction (LVEF) is the most important parameter in the assessment of cardiac function. A machine-learning algorithm was trained to guide ultrasound-novices to acquire diagnostic echocardiography images. The artificial intell...
Male Female Ventricular Function, Left Pilot Projects Reproducibility of Results Stroke Volume Clinical Competence Aged Humans Echocardiography Predictive Value of Tests Adult Machine Learning Middle Aged Heart Diseases Students, Medical Image Interpretation, Computer-Assisted
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Prognostic importance of Kidney, Heart and Interstitial lung diseases (KHI triad) in PH: A machine learning study.

Archives of cardiovascular diseases Sep 2, 2020
BACKGROUND: Pulmonary hypertension (PH) is a heterogeneous, severe and progressive disease with an impact on quality of life and life-expectancy despite specific therapies.
Heart Diseases France Pulmonary Arterial Hypertension Prognosis Female Risk Factors Aged Aged, 80 and over Humans Cluster Analysis Male Machine Learning Kidney Diseases Middle Aged Phenotype Registries Risk Assessment Prospective Studies Lung Diseases, Interstitial Comorbidity Health Status Data Mining
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Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.

The international journal of cardiovascular imaging Jul 16, 2020
To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 year...
Magnetic Resonance Imaging, Cine Aged, 80 and over Middle Aged Feasibility Studies Diagnosis, Computer-Assisted Humans Young Adult Heart Ventricles Deep Learning Adult Retrospective Studies Heart Diseases Adolescent Male Female Image Interpretation, Computer-Assisted Predictive Value of Tests Ventricular Function, Left Reproducibility of Results Automation Aged
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