AI Medical Compendium Topic:
Echocardiography

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Artificial Intelligence Models for Heart Chambers Segmentation from 2D Echocardiographic Images: A Scoping Review.

Studies in health technology and informatics
Echocardiography (echo) is a non-invasive, safe, widely available imaging modality that is frequently used to assess the heart structure and function. Accurate heart chamber segmentation is an essential step to quantify certain parameters, including ...

Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future.

Reviews in cardiovascular medicine
Artificial Intelligence (AI) performs human intelligence-dependant tasks using tools such as Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of cardiovascular medicine, and increasingly employed to revolutioni...

Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Rheumatic heart disease (RHD) affects an estimated 39 million people worldwide and is the most common acquired heart disease in children and young adults. Echocardiograms are the gold standard for diagnosis of RHD, but there is a shortage ...

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use.

JAMA cardiology
IMPORTANCE: Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on mor...

[Progress in cardiac imaging: from echocardiography to multimodality imaging].

Giornale italiano di cardiologia (2006)
In the last few decades, echocardiography has represented one of the technological fields with the fastest evolution and progress. As a non-invasive method at relative low cost, it is also suitable for the future to an increasingly integrated use in ...

Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.

Journal of the American College of Cardiology
BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise.

Classification of Aortic Stenosis Using ECG by Deep Learning and its Analysis Using Grad-CAM.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using ECG (Electrocardiogram) images by the deep learning whose training ECG images are annotated by the diagnoses given by the medical doctor who observes the echo...

Artificial intelligence capable of detecting left ventricular hypertrophy: pushing the limits of the electrocardiogram?

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology

Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: Although left ventricular hypertrophy (LVH) has a high incidence and clinical importance, the conventional diagnosis criteria for detecting LVH using electrocardiography (ECG) has not been satisfied. We aimed to develop an artificial intelligen...