Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

By Sukrit Narula, Khader Shameer, Alaa Mabrouk Salem Omar, Joel T. Dudley, Partho P. Sengupta. 2016

368 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Sameer Quazi
2. T. Marwick
3. Jeroen J. Bax
4. Daniel Rueckert
5. James K. Min

Citations in the corpus

• Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling (2018, background): “Indeed, early work has shown the potential of machine learning algorithms in distinguishing benign from pathological hypertrophy from multiple manually-derived cardiac parameters [6].”
• Artificial Intelligence in Cardiology. (2018, methodology): “We have previously used such “ensemble” methods to automate analysis of echocardiography imaging (22,23).”
• Artificial intelligence and machine learning in precision and genomic medicine (2021, methodology): “[158] used ML to distinguish hypertrophic cardiomyopathy from normal heart hypertrophy.”
• Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. (2018, methodology): “43,44 Finally, ML has also been utilized to detect and characterize valvular and anatomic pathology, as well as to enhance the quality of existing echocardiograms 16–18,45–50 ( Figure 5 and Table 2 ).”
• Artificial Intelligence in Cardiovascular Imaging (2019, background): “Radiology has been leading this field, and cardiovascular imaging is seeking to parallel its advances for optimizing the accuracy and quality of images (16,90,91).”
• Machine learning in cardiovascular medicine: are we there yet? (2018, methodology): “7 In another similar example, we developed a cognitive machine learning-based classifier to distinguish between constrictive pericarditis and restrictive cardiomyopathy.”
• Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging (2023, background): “Its exponential growth in echocardiography is evident, with automated algorithms aiding in the interpretation of cardiac structure parameters, mitigating interobserver variability, and enhancing diagnostic precision [49].”
• Deep Learning for Prediction of Obstructive Disease from Fast Myocardial Perfusion SPECT: A Multicenter Study (2018, neutral): “Number 620 1,018 1-vessel disease — 466 (46) 2-vessel disease — 325 (32) 3-vessel disease — 227 (22) Age, yrs 62.”
• Fast and accurate classification of echocardiograms using deep learning (2017, background): “The simplicity of training in deep learning represents a significant advantage over earlier machine-learning methods, which have sometimes been applied to echocardiography but often require time-consuming and operator-dependent manual selection and annotation of features, such as manually tracing th”