Automated Analysis of Doppler Echocardiographic Videos as a Screening Tool for Valvular Heart Diseases

Yang, Fang; Chen, Xiaotian; Lin, Xixiang; Chen, Zhanghua; Wang, Wenjun; Liu, Bohan; Li, Yao; Pu, Haitao; Zhang, Liwei; Huang, Dangsheng; Zhang, Meiqing; Li, Xin; Wang, Hui; Wang, Yueheng; Guo, Huayuan; Deng, Yao; Zhang, Lu; Zhong, Qin; Li, Zongren; Yu, Liheng; Duan, Yongjie; Zhang, Peifang; Wu, Zhenzhou; Burkhoff, Daniel; Wang, Qiushuang; He, Kunlun

doi:10.1016/j.jcmg.2021.08.015

Cited by 31 publications

(19 citation statements)

References 17 publications

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“…We labeled 33,404 images to develop a method to classify 29 standard views and then selected the three apical views required for the subsequent analysis. View selection was performed using a Xception Net neural network model according to methods that were previously described ( 18 ).…”

Section: Methodsmentioning

confidence: 99%

Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction

Lin

Yang

Chen³

et al. 2022

Front. Cardiovasc. Med.

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ObjectiveTo compare the performance of a newly developed deep learning (DL) framework for automatic detection of regional wall motion abnormalities (RWMAs) for patients presenting with the suspicion of myocardial infarction from echocardiograms obtained with portable bedside equipment versus standard equipment.BackgroundBedside echocardiography is increasingly used by emergency department setting for rapid triage of patients presenting with chest pain. However, compared to images obtained with standard equipment, lower image quality from bedside equipment can lead to improper diagnosis. To overcome these limitations, we developed an automatic workflow to process echocardiograms, including view selection, segmentation, detection of RWMAs and quantification of cardiac function that was trained and validated on image obtained from bedside and standard equipment.MethodsWe collected 4,142 examinations from one hospital as training and internal testing dataset and 2,811 examinations from other hospital as the external test dataset. For data pre-processing, we adopted DL model to automatically recognize three apical views and segment the left ventricle. Detection of RWMAs was achieved with 3D convolutional neural networks (CNN). Finally, DL model automatically measured the size of cardiac chambers and left ventricular ejection fraction.ResultsThe view selection model identified the three apical views with an average accuracy of 96%. The segmentation model provided good agreement with manual segmentation, achieving an average Dice of 0.89. In the internal test dataset, the model detected RWMAs with AUC of 0.91 and 0.88 respectively for standard and bedside ultrasound. In the external test dataset, the AUC were 0.90 and 0.85. The automatic cardiac function measurements agreed with echocardiographic report values (e. g., mean bias is 4% for left ventricular ejection fraction).ConclusionWe present a fully automated echocardiography pipeline applicable to both standard and bedside ultrasound with various functions, including view selection, quality control, segmentation, detection of the region of wall motion abnormalities and quantification of cardiac function.

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Section: Methodsmentioning

confidence: 99%

Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction

Lin

Yang

Chen³

et al. 2022

Front. Cardiovasc. Med.

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“…The identification of higher-risk subjects in this study (higher aortic valve calcium scores, larger late gadolinium enhancement, higher biomarker levels, and greater incidences of negative clinical outcomes) has the potential to optimize the timing of aortic valve replacements [ 41 ]. In another recent publication including a large training ( n = 1335) and validated ( n = 311) cohort, a framework for the automatic screening of echocardiographic videos for mitral and aortic disease was developed [ 42 ]. This deep learning algorithm was able to classify echocardiographic views, detect the presence of valve heart disease, and quantify disease severity with high accuracy (AOC > 0.88 for all left heart valve diseases) [ 42 ].…”

Section: The Role Of Ai In Identifying Disease Statesmentioning

confidence: 99%

“…In another recent publication including a large training ( n = 1335) and validated ( n = 311) cohort, a framework for the automatic screening of echocardiographic videos for mitral and aortic disease was developed [ 42 ]. This deep learning algorithm was able to classify echocardiographic views, detect the presence of valve heart disease, and quantify disease severity with high accuracy (AOC > 0.88 for all left heart valve diseases) [ 42 ]. These novel findings support the effectiveness of an automated framework, trained on routine echocardiographic datasets, to screen, classify, and quantify the severity of conditions that are frequent in medical practice.…”

Section: The Role Of Ai In Identifying Disease Statesmentioning

confidence: 99%

The Role of Artificial Intelligence in Echocardiography

et al. 2023

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Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

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“…This is a potential advantage of a DL approach, enabling multibeat analysis in a fraction of the time it would take a sonographer or echocardiographer, while improving reliability by reducing intraobserver and interobserver variability (eFigure 4G in the Supplement). Despite increased complexity and sophistication of DL approaches to echocardiographic data, many underexplored areas remain; while applications of DL methodology to Doppler echocardiography are increasing, few investigators have implemented DL for analysis of 3-dimensional echocardiography.…”

Section: Examples Of Deep Learning In Cardiovascular Imagingmentioning

confidence: 99%

Deep Learning for Cardiovascular Imaging

Wehbe,

Katsaggelos,

Hammond

et al. 2023

JAMA Cardiol

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ImportanceArtificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology.ObservationsAt its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI.Conclusions and RelevanceDeep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.

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Automated Analysis of Doppler Echocardiographic Videos as a Screening Tool for Valvular Heart Diseases

Cited by 31 publications

References 17 publications

Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction

Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction

The Role of Artificial Intelligence in Echocardiography

Deep Learning for Cardiovascular Imaging

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