Artificial Intelligence for Dynamic Echocardiographic Tricuspid Valve Analysis: A New Tool in Echocardiography

Fatima, Huma; Mahmood, Feroze; Sehgal, Sankalp; Belani, Kiran; Sharkey, Aidan; Chaudhary, Omar; Baribeau, Yanick; Matyal, Robina; Khabbaz, Kamal R.

doi:10.1053/j.jvca.2020.04.056

Cited by 13 publications

(14 citation statements)

References 6 publications

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“…1-6,11-14,28- the greater diagnostic accuracy may be. [1][2][3][4][5][6][11][12][13][14][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] Madani et al 28 employed DL for view classification of thousands of echocardiographic acquisitions that captured many real world clinical variations. Another successfully trained AI-assisted model for view interpretation and classification has been reported by Zhang et al 29 A recently published multicohort study has demonstrated good accuracy of DL model in interpreting and classifying both two-dimensional and Doppler echocardiographic acquisitions.…”

Section: Strengths and Potential Of Ai In Cardiovascular Imaging: The...mentioning

confidence: 99%

Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point‐of‐view from “the other side of the coin”

Dell'Angela

Nicolosi²

2022

J of Clinical Ultrasound

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Cardiovascular imaging has achieved a crucial role in the management of cardiovascular diseases. In this field, echocardiography advantages include wide availability, portability, and affordability, at a relatively low cost. However, echocardiographic assessment requires highly trained operators, and implies high observer variability, as compared with the other cardiac imaging modalities. Hence, artificial intelligence might be extremely helpful. From the point‐of‐view of the peripheral “Spoke” Hospital potential user (“the other side of the coin”), artificial intelligence development appears very slow in the clinical arena. Many limitations are still present, and require full involvement, cooperation, and coordination of professional operators into Hub‐and‐Spoke network.

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Section: Strengths and Potential Of Ai In Cardiovascular Imaging: The...mentioning

confidence: 99%

Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point‐of‐view from “the other side of the coin”

Dell'Angela

Nicolosi²

2022

J of Clinical Ultrasound

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“…echocardiographic machines produced by the same vendor (figure 3). 27 Non-commercial programs have also been developed to aid valve annular and leaflet segmentation (table 2). These programs focus on CNN development using mitral valve images.…”

Section: Reviewmentioning

confidence: 99%

“…Due to the proprietary nature of commercial software packages, details on the included AI algorithms are not available, although it is likely that current iterations include some form of AI analytics (online supplemental table 1). 25–27 Overall, these commercial packages have a few limitations. Some are technically semiautomated processes that require expert initialisation and others can only be applied to images generated from echocardiographic machines produced by the same vendor (figure 3).…”

Section: Ai To Improve Echocardiographic Image Valve Assessmentmentioning

confidence: 99%

Artificial intelligence for the echocardiographic assessment of valvular heart disease

Nedadur

Wang

Tsang

2022

Heart

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Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.

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“…AI-based valve assessment software algorithms integrating 2D and 3D echocardiographic parameters with automated quantification of disease severity have shown to reduce time to analyze cardiac structures and provide good reproducibility with minimal user intervention. Their implementation could improve diagnostic and prognostic stratification for surgical and interventional procedures [ 146 , 147 ].…”

Section: Secondary Mitral and Tricuspid Regurgitationmentioning

confidence: 99%

Echocardiographic Advances in Dilated Cardiomyopathy

Faggiano

Avallone

Gentile

et al. 2021

JCM

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Although the overall survival of patients with dilated cardiomyopathy (DCM) has improved significantly in the last decades, a non-negligible proportion of DCM patients still shows an unfavorable prognosis. DCM patients not only need imaging techniques that are effective in diagnosis, but also suitable for long-term follow-up with frequent re-evaluations. The exponential growth of echocardiography’s technology and performance in recent years has resulted in improved diagnostic accuracy, stratification, management and follow-up of patients with DCM. This review summarizes some new developments in echocardiography and their promising applications in DCM. Although nowadays cardiac magnetic resonance (CMR) remains the gold standard technique in DCM, the echocardiographic advances and novelties proposed in the manuscript, if properly integrated into clinical practice, could bring echocardiography closer to CMR in terms of accuracy and may certify ultrasound as the technique of choice in the follow-up of DCM patients. The application in DCM patients of novel echocardiographic techniques represents an interesting emergent research area for scholars in the near future.

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Artificial Intelligence for Dynamic Echocardiographic Tricuspid Valve Analysis: A New Tool in Echocardiography

Cited by 13 publications

References 6 publications

Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point‐of‐view from “the other side of the coin”

Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point‐of‐view from “the other side of the coin”

Artificial intelligence for the echocardiographic assessment of valvular heart disease

Echocardiographic Advances in Dilated Cardiomyopathy

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