Quality Assessment of Echocardiographic Cine Using Recurrent Neural Networks: Feasibility on Five Standard View Planes

Abdi, Ali A.; Luong, Christina; Tsang, Teresa S.M.; Jue, John; Gin, Ken; Yeung, Darwin F.; Hawley, Dale; Rohling, Robert; Abolmaesumi, Purang

doi:10.1007/978-3-319-66179-7_35

Cited by 27 publications

(16 citation statements)

References 9 publications

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“…Perrin et al (2017) and Narula et al (2016) have used it for evaluation of cardiac function. Abdi et al (2017) used it to automatically assess the quality of up to five views using a regression-based recurrent approach. Recently, Gao et al (2017) used CNNs for classifying eight different cardiac views using a method fusing hand-crafted and learned features.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks

Østvik

Smistad

Aase³

et al. 2019

Ultrasound in Medicine & Biology

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Transthoracic echocardiography examinations are usually performed according to a protocol comprising different probe postures providing standard views of the heart. These are used as a basis when assessing cardiac function, and it is essential that the morphophysiological representations are correct. Clinical analysis is often initialized with the current view, and automatic classification can thus be useful in improving today's workflow. In this article, convolutional neural networks (CNNs) are used to create classification models predicting up to seven different cardiac views. Data sets of 2-D ultrasound acquired from studies totaling more than 500 patients and 7000 videos were included. State-of-the-art accuracies of (98.3±0.6)% and (98.9±0.6)% on single frames and sequences, respectively, and real-time performance with (4.4±0.3) ms per frame was achieved. Further, it was found that CNNs have the potential for use in automatic multiplanar reformatting and orientation guidance. Using 3-D data to train models applicable for 2-D classification, we achieved a median deviation of (4±3) • from the optimal orientations.

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Section: Introductionmentioning

confidence: 99%

Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks

Østvik

Smistad

Aase³

et al. 2019

Ultrasound in Medicine & Biology

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“…They used three-fold cross validation and reported an error comparable to intra-rater reliability (mean absolute error: 0.71 ± 0.58). Abdi et al [ 23 ] later extended their previous work and trained a CNN regression architecture that includes five regression models with the same weights in the first few layers for assessing the quality of cine loops across five standard view planes (i.e., apical 2, 3, and 4 chamber views and parasternal short axis views at papillary muscle and aortic valve levels). Their dataset included 2435 cine clips, and they achieved an average of 85% accuracy compared to gold standard scores assigned by experienced echo sonographers on 20% of the dataset.…”

Section: Methods and Results: Automated Echo Interpretationmentioning

confidence: 99%

Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review

Akkus

Aly

Attia

et al. 2021

JCM

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Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.

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“…Quality score assessment and other tasks were also targeted using echocardiography. In [216] the authors created a method for reducing operator variability in data acquisition by computing an echo quality score for real-time feedback. The model consisted of convolutional layers to extract features from the input echo cine and recurrent layers to use the sequential information in the echo cine loop.…”

Section: Echocardiographymentioning

confidence: 99%

Deep Learning in Cardiology

Bizopoulos

Koutsouris

2019

IEEE Rev. Biomed. Eng.

143

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The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

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Quality Assessment of Echocardiographic Cine Using Recurrent Neural Networks: Feasibility on Five Standard View Planes

Cited by 27 publications

References 9 publications

Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks

Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks

Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review

Deep Learning in Cardiology

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