An attention‐based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy

Kampaktsis, Polydoros N.; Bohoran, Tuan A.; Lebehn, Mark; McLaughlin, Laura; Leb, Jay; Liu, Zhonghua; Moustakidis, Serafeim; Siouras, Athanasios; Singh, Anvesha; Hahn, Rebecca T.; McCann, Gerry P.; Giannakidis, Archontis

doi:10.1111/echo.15719

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…The first study investigates an attention-based deep learning (DL) approach for quantifying the right ventricle in 2D echocardiography, demonstrating both feasibility and promising accuracy. This model was evaluated against cardiac magnetic resonance imaging as the reference standard [ 36 ]. Murayama M. et al created a fully automated deep learning (DL) tool designed to estimate right ventricular ejection fraction (RVEF) from two-dimensional echocardiographic videos of apical four-chamber views in patients with precapillary pulmonary hypertension (PH).…”

Section: Resultsmentioning

confidence: 99%

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

Patrascanu,

Tutunaru,

Musat

et al. 2024

JPM

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Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.

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Section: Resultsmentioning

confidence: 99%

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

Patrascanu,

Tutunaru,

Musat

et al. 2024

JPM

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Towards deep learning methods for quantification of the right ventricle using 2D echocardiography

Kampaktsis,

Moustakidis,

Siasos

et al. 2024

Future Cardiology

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Deep learning to assess right ventricular ejection fraction from two‐dimensional echocardiograms in precapillary pulmonary hypertension

Murayama,

Sugimori,

Yoshimura

et al. 2024

Echocardiography

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BackgroundPrecapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic function. Two‐dimensional (2D) echocardiography is the most performed cardiac imaging tool to assess RV systolic function; however, an accurate evaluation requires expertise. We aimed to develop a fully automated deep learning (DL)‐based tool to estimate the RV ejection fraction (RVEF) from 2D echocardiographic videos of apical four‐chamber views in patients with precapillary PH.MethodsWe identified 85 patients with suspected precapillary PH who underwent cardiac magnetic resonance imaging (MRI) and echocardiography. The data was divided into training (80%) and testing (20%) datasets, and a regression model was constructed using 3D‐ResNet50. Accuracy was assessed using five‐fold cross validation.ResultsThe DL model predicted the cardiac MRI‐derived RVEF with a mean absolute error of 7.67%. The DL model identified severe RV systolic dysfunction (defined as cardiac MRI‐derived RVEF < 37%) with an area under the curve (AUC) of .84, which was comparable to the AUC of RV fractional area change (FAC) and tricuspid annular plane systolic excursion (TAPSE) measured by experienced sonographers (.87 and .72, respectively). To detect mild RV systolic dysfunction (defined as RVEF ≤ 45%), the AUC from the DL‐predicted RVEF also demonstrated a high discriminatory power of .87, comparable to that of FAC (.90), and significantly higher than that of TAPSE (.67).ConclusionThe fully automated DL‐based tool using 2D echocardiography could accurately estimate RVEF and exhibited a diagnostic performance for RV systolic dysfunction comparable to that of human readers.

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An attention‐based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy

Cited by 3 publications

References 37 publications

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

Towards deep learning methods for quantification of the right ventricle using 2D echocardiography

Deep learning to assess right ventricular ejection fraction from two‐dimensional echocardiograms in precapillary pulmonary hypertension

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