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\begin{document}

\title{An attention-based deep learning method for right ventricular
quantification using 2D echocardiography: feasibility and accuracy}



\author[1]{Polydoros Kampaktsis}%
\author[2]{Tuan A. Bohoran}%
\author[1]{Laura McLaughlin}%
\author[3]{Jay Leb S}%
\author[4]{Zhonghua Liu}%
\author[5]{Serafeim Moustakidis}%
\author[5]{Athanasios Siouras}%
\author[6]{Anvesha Singh}%
\author[7]{Rebecca Hahn}%
\author[6]{Gerry P. McCann}%
\author[2]{Archontis Giannakidis}%
\affil[1]{Columbia University Irving Medical Center}%
\affil[2]{Nottingham Trent University}%
\affil[3]{Columbia University Department of Radiology}%
\affil[4]{Columbia University Department of Biostatistics}%
\affil[5]{Panepistemio Thessalias Tmema Iatrikes}%
\affil[6]{University of Leicester Department of Cardiovascular Sciences}%
\affil[7]{Columbia University Division of Cardiology}%


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  \date{June 05, 2024}


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\begin{abstract}
\textbf{Aim:} To test the feasibility and accuracy of a new
attention-based deep learning (DL) method for right ventricular (RV)
quantification using 2D echocardiography (2DE) with cardiac magnetic
resonance imaging (CMR) as reference. \textbf{Methods and results:} We
retrospectively analyzed images from 50 adult patients (median age 51,
interquartile range 32-62 42\% women) who had undergone CMR within 1
month of 2DE. RV planimetry of the myocardial border was performed in
end-diastole (ED) and end-systole (ES) for 8 standardized 2DE RV views
with calculation of areas. The DL model comprised a Feature Tokenizer
module and a stack of Transformer layers. Age, gender and calculated
areas were used as inputs, and the output was RV volume in ED/ES. The
dataset was randomly split into training, validation and testing subsets
(35, 5 and 10 patients respectively). Mean RVEDV, RVESV and RV ejection
fraction (EF) were 163\selectlanguage{ngerman}±70ml, 82±42ml and 51±8\% respectively without
differences among the subsets. The proposed method achieved good
prediction of RV volumes (R \textsuperscript{2}=0.953, absolute
percentage error {[}APE{]}=9.75±6.23\%) and RVEF (APE=7.24±4.55\%). Per
CMR, there was 1 patient with RV dilatation and 3 with RV dysfunction in
the testing dataset. The DL model detected RV dilatation in 1/1 case and
RV dysfunction in 4/3 cases. \textbf{Conclusions:} An attention-based DL
method for 2DE RV quantification showed feasibility and promising
accuracy. The method requires validation in larger cohorts with wider
range of RV size and function. Further research will focus on the
reduction of the number of required 2DE to make the method clinically
applicable.%
\end{abstract}\selectlanguage{ngerman}%



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\includegraphics[width=0.70\columnwidth]{figures/Figure-1---DLarchitecture/Figure-1---DLarchitecture}

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\includegraphics[width=0.70\columnwidth]{figures/Figure-2---Predicted-vs-ground-truth/Figure-2---Predicted-vs-ground-truth}

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