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

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Kampaktsis, PN, Bohoran, TA, Lebehn, M, McLaughlin, L, Leb, J, Liu, Z, Moustakidis, S, Siouras, A, Singh, A, Hahn, RT, McCann, GP and Giannakidis, A ORCID: https://orcid.org/0000-0001-7403-923X, 2024. An attention‐based deep learning method for right ventricular quantification using 2D echocardiography: feasibility and accuracy. Echocardiography, 41 (1): e15719. ISSN 0742-2822

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Official URL: https://doi.org/10.1111/echo.15719

Abstract

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.

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 eight 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 ± 70 mL, 82 ± 42 mL and 51% ± 8% respectively without differences among the subsets. The proposed method achieved good prediction of RV volumes (R2 = .953, absolute percentage error [APE] = 9.75% ± 6.23%) and RVEF (APE = 7.24% ± 4.55%). Per CMR, there was one patient with RV dilatation and three with RV dysfunction in the testing dataset. The DL model detected RV dilatation in 1/1 case and RV dysfunction in 4/3 cases.

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.

Item Type:	Journal article
Publication Title:	Echocardiography
Creators:	Kampaktsis, P.N., Bohoran, T.A., Lebehn, M., McLaughlin, L., Leb, J., Liu, Z., Moustakidis, S., Siouras, A., Singh, A., Hahn, R.T., McCann, G.P. and Giannakidis, A.
Publisher:	Wiley
Date:	2024
Volume:	41
Number:	1
ISSN:	0742-2822
Identifiers:	Number Type 10.1111/echo.15719 DOI 1849298 Other
Rights:	This is the peer reviewed version of the following article: KAMPAKTSIS, P.N., BOHORAN, T.A., LEBEHN, M., MCLAUGHLIN, L., LEB, J., LIU, Z., MOUSTAKIDIS, S., SIOURAS, A., SINGH, A., HAHN, R.T., MCCANN, G.P. and GIANNAKIDIS, A., 2024. An attention‐based deep learning method for right ventricular quantification using 2D echocardiography: feasibility and accuracy. Echocardiography, 41 (1): e15719, which has been published in final form at https://doi.org/10.1111/echo.15719. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	10 Jan 2024 09:21
Last Modified:	21 Dec 2024 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/50641