Automated assessment of transthoracic echocardiogram image quality using deep neural networks

Labs, Robert B.; Vrettos, Apostolos; Loo, Jonathan P.; Zolgharni, Massoud

doi:10.1016/j.imed.2022.08.001

Cited by 7 publications

(9 citation statements)

References 24 publications

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“…Labs et al predicted transthoracic echocardiogram image quality with 96.2% accuracy using a dataset of 33,784 images in parasternal long-axis and apical 4-chamber views [ 19 ]. Loung et al achieved 87.0% in image quality prediction using a dataset of 14,086 images in nine views [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…In comparison to many studies that aim to highlight the potential benefits of the use of AI and the development of AI models [ 5 , 11 , 12 , 13 , 14 , 15 , 16 ], only Sengupta et al, Huang et al, Nagata et al, and Saikhan et al analyzed the impact of image quality on the accuracy of clinically relevant echocardiographic measurements and outcomes regarding diagnosis and prognosis [ 1 , 2 , 3 , 4 ]. Moreover, a small number of studies aim to predict image quality from two-dimensional raw, unprocessed echocardiogram images [ 17 , 18 , 19 ]. All of them use convolutional neural networks [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, a small number of studies aim to predict image quality from two-dimensional raw, unprocessed echocardiogram images [ 17 , 18 , 19 ]. All of them use convolutional neural networks [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Overall, automated evaluation of the echocardiogram image will lead to a more objective estimation of the image quality indices [ 19 ]. Such a system can aid in the training of new echocardiographers.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality

Nazar,

Daniłowicz-Szymanowicz

2024

Life

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High-quality echocardiogram images are the cornerstone of accurate and reliable measurements of the heart. Therefore, this study aimed to develop, validate and compare machine learning and deep learning algorithms for accurate and automated assessment of transthoracic echocardiogram image quality. In total, 4090 single-frame two-dimensional transthoracic echocardiogram images were used from apical 4-chamber, apical 2-chamber and parasternal long-axis views sampled from 3530 adult patients. The data were extracted from CAMUS and Unity Imaging open-source datasets. For every raw image, additional grayscale block histograms were developed. For block histogram datasets, six classic machine learning algorithms were tested. Moreover, convolutional neural networks based on the pre-trained EfficientNetB4 architecture were developed for raw image datasets. Classic machine learning algorithms predicted image quality with 0.74 to 0.92 accuracy (AUC 0.81 to 0.96), whereas convolutional neural networks achieved between 0.74 and 0.89 prediction accuracy (AUC 0.79 to 0.95). Both approaches are accurate methods of echocardiogram image quality assessment. Moreover, this study is a proof of concept of a novel method of training classic machine learning algorithms on block histograms calculated from raw images. Automated echocardiogram image quality assessment methods may provide additional relevant information to the echocardiographer in daily clinical practice and improve reliability in clinical decision making.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality

Nazar,

Daniłowicz-Szymanowicz

2024

Life

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“…DL methods, specifically convolutional neural networks (CNNs) [17,18] and long short-term memory (LSTM) cells for regression [12,[18][19][20][21][22], have been applied to automate echocardiographic image quality assessment, with varying degrees of success.…”

Section: Related Workmentioning

confidence: 99%

A computer vision pipeline for fully automated echocardiogram interpretation

Lane¹

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Cardiovascular disease is the leading cause of global mortality and continues to place a significant burden, in economic and resource terms, upon health services. A 2-dimensional transthoracic echocardiogram captures high spatial and temporal images and videos of the heart and is the modality of choice for the rapid assessment of heart function and structure due to it’s non-invasive nature and lack of ionising radiation. The challenging process of analysing echocardiographic images is currently manually performed by trained experts, though this process is vulnerable to intra- and inter-observer variability and is highly time-consuming. Additionally, echocardiographic images suffer from varying degrees of noise and vary drastically in terms of image quality. Exponential advancements in the fields of artificial intelligence, deep learning and computer vision have enabled the rapid development of automated systems capable of high-precision tasks, often out-performing human experts. This thesis aims to investigate the applicability of applying deep learning methods to automate key processes in the modern echocardiographic laboratory. Namely, view classification, quality assessment, cardiac phase detection, segmentation of the left ventricle and keypoint detection on tissue Doppler imaging strips. State-of-the-art deep learning architectures were applied to each task, and evaluated against ground-truth annotations provided by trained experts. The datasets used throughout each Chapter are diverse and, in some cases, have been made public for the benefit of the research community. To encourage transparency and openness, all code and model weights have been published. Should automated deep learning systems, both online (in terms of providing real-time feedback) and offline (behind the scenes), become integrated within clinical practice, there is great potential for improved accuracy and efficiency, thus improving patient outcomes. Furthermore, health services could save valuable resources such as time and money.

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Regressing Simulation to Real: Unsupervised Domain Adaptation for Automated Quality Assessment in Transoesophageal Echocardiography

Xu,

Jin,

Martin

et al. 2023

Lecture Notes in Computer Science

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Automated assessment of transthoracic echocardiogram image quality using deep neural networks

Cited by 7 publications

References 24 publications

Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality

Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality

A computer vision pipeline for fully automated echocardiogram interpretation

Regressing Simulation to Real: Unsupervised Domain Adaptation for Automated Quality Assessment in Transoesophageal Echocardiography

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