Automated technique for coronary artery disease characterization and classification using DD-DTDWT in ultrasound images

Raghavendra, U.; Fujita, Hamido; Gudigar, Anjan; Shetty, Ranjan; Nayak, Krishnananda; Pai, Umesh; Samanth, Jyothi; Acharya, Rajendra

doi:10.1016/j.bspc.2017.09.030

Cited by 56 publications

(33 citation statements)

References 56 publications

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“…Whilst this is comparable to other functional assessments of coronary artery disease, it still means that approximately one in every five patients could potentially be misdiagnosed ( 52 ). In order to enhance the accuracy of stress echocardiography, machine learning models have been evaluated as means to identify and quantify inducible wall motion abnormalities ( 53 , 54 , 55 , 56 ). In one study, Omar et al used imaging derived models of 3D motion at rest and stress within random forests, support vector machines and a deep learning approach consisting of a convolutional neural network.…”

Section: State Of the Art – Future And Potential Applicationsmentioning

confidence: 99%

“…They found that the convolutional neural network provided the most sensitive model, with a sensitivity of 81.1% in a training dataset compared to expert operator interpretation ( 55 ). In another study, an unsupervised learning model was used to detect 12 features for linear discrimination, which could differentiate between patients with obstructive disease and normal responses through use of a new coronary artery disease risk index ( 54 ). The majority of studies to date have been on relatively small datasets, without adequate testing validation or have only compared against expert readers rather than outcome.…”

Section: State Of the Art – Future And Potential Applicationsmentioning

confidence: 99%

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Artificial intelligence and echocardiography

Alsharqi

Woodward

Mumith³

et al. 2018

Echo Res Pract

205

126

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Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.

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Section: State Of the Art – Future And Potential Applicationsmentioning

confidence: 99%

Section: State Of the Art – Future And Potential Applicationsmentioning

confidence: 99%

Artificial intelligence and echocardiography

Alsharqi

Woodward

Mumith³

et al. 2018

Echo Res Pract

205

126

View full text Add to dashboard Cite

show abstract

“…An overview of the studies for the diagnostic ability of current deep learning models in the field of echocardiography in Table 1 [16][17][18][19][20][21][22][23][24][25][26][27]. The accuracy of AI models has been achieved around 80-90%.…”

Section: Overview Of Deep Learningmentioning

confidence: 99%

Steps to use artificial intelligence in echocardiography

Kusunose

2020

J Echocardiogr

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Artificial intelligence (AI) has influenced every field of cardiovascular imaging in all phases from acquisition to reporting. Compared with computed tomography and magnetic resonance imaging, there is an issue of high observer variation in the interpretation of echocardiograms. Therefore, AI can help minimize the observer variation and provide accurate diagnosis in the field of echocardiography. In this review, we summarize the necessity for automated diagnosis in the echocardiographic field, and discuss the results of AI application to echocardiography and future perspectives. Currently, there are two roles for AI in cardiovascular imaging. One is the automation of tasks performed by humans, such as image segmentation, measurement of cardiac structural and functional parameters. The other is the discovery of clinically important insights. Most reported applications were focused on the automation of tasks. Moreover, algorithms that can obtain cardiac measurements are also being reported. In the next stage, AI can be expected to expand and enrich existing knowledge. With the continual evolution of technology, cardiologists should become well versed in this new knowledge of AI and be able to harness it as a tool. AI can be incorporated into everyday clinical practice and become a valuable aid for many healthcare professionals dealing with cardiovascular diseases.

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“…Table summarizes the diagnostic ability of current machine-learning models in the field of echocardiography. 16,[18][19][20][21][22][23][24][25] The remainder of this review focuses on previously published deep learning approaches in echocardiography, view classifications, automated analysis of size and function, diagnosis of cardiovascular diseases, and diastolic dysfunction.…”

Section: Direction Of Ai In Echocardiographymentioning

confidence: 99%

Utilization of Artificial Intelligence in Echocardiography

Kusunose

Haga

Abe

et al. 2019

Circ J

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Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echocardiography, speckle-tracking, semi-automated analysis, etc.), the final decision on analysis is strongly dependent on operator experience. Diagnostic errors are a major unresolved problem. Moreover, not only can cardiologists differ from one another in image interpretation, but also the same observer may come to different findings when a reading is repeated. Daily high workloads in clinical practice may lead to this error, and all cardiologists require precise perception in this field. Artificial intelligence (AI) has the potential to improve analysis and interpretation of medical images to a new stage compared with previous algorithms. From our comprehensive review, we believe AI has the potential to improve accuracy of diagnosis, clinical management, and patient care. Although there are several concerns about the required large dataset and "black box" algorithm, AI can provide satisfactory results in this field. In the future, it will be necessary for cardiologists to adapt their daily practice to incorporate AI in this new stage of echocardiography.

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Automated technique for coronary artery disease characterization and classification using DD-DTDWT in ultrasound images

Cited by 56 publications

References 56 publications

Artificial intelligence and echocardiography

Artificial intelligence and echocardiography

Steps to use artificial intelligence in echocardiography

Utilization of Artificial Intelligence in Echocardiography

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