AIMC Topic: Echocardiography

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Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms.

Nature communications
Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of...

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

Nature medicine
We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is unde...

Myocardial Function Imaging in Echocardiography Using Deep Learning.

IEEE transactions on medical imaging
Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated efficacy, everyday clinical...

Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning.

Pediatric cardiology
The heart murmur associated with atrial septal defects is often faint and can thus only be detected by chance. Although electrocardiogram examination can prompt diagnoses, identification of specific findings remains a major challenge. We demonstrate ...

A deep learning approach for the automatic recognition of prosthetic mitral valve in echocardiographic images.

Computers in biology and medicine
The first step in the automatic evaluation of the cardiac prosthetic valve is the recognition of such valves in echocardiographic images. This research surveyed whether a deep convolutional neural network (DCNN) could improve the recognition of prost...

Multibeat echocardiographic phase detection using deep neural networks.

Computers in biology and medicine
BACKGROUND: Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is important, yet challenging, for human experts. Manual frame selection is subject to uncertainty, affecting crucial clinical measurements, ...

Echocardiographic image multi-structure segmentation using Cardiac-SegNet.

Medical physics
PURPOSE: Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast-to-noise ratio and speckle noise o...

Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-Ray.

The Canadian journal of cardiology
BACKGROUND: To accurately diagnose and control heart failure (HF), it is important to carry out a simple assessment of elevated pulmonary arterial wedge pressure (PAWP). The aim of this study was to develop and validate an objective method for detect...

A deep learning approach with temporal consistency for automatic myocardial segmentation of quantitative myocardial contrast echocardiography.

The international journal of cardiovascular imaging
Quantitative myocardial contrast echocardiography (MCE) has been proved to be valuable in detecting myocardial ischemia. During quantitative MCE analysis, myocardial segmentation is a critical step in determining accurate region of interests (ROIs). ...

Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality.

Nature biomedical engineering
Machine learning promises to assist physicians with predictions of mortality and of other future clinical events by learning complex patterns from historical data, such as longitudinal electronic health records. Here we show that a convolutional neur...