Key result
Deep learning using 2D echo predicts RV volumes with ~10% error compared to CMR.
Why the study?
To evaluate the feasibility and accuracy of a new attention-based deep learning method for right ventricular quantification using 2D echocardiography compared with cardiac magnetic resonance imaging.
Does an attention-based deep learning method using 2D echocardiography accurately quantify right ventricular volumes and ejection fraction compared to CMR in adult patients?
Observational (n=50)
Single-blind
No
Does an attention-based deep learning method using 2D echocardiography accurately quantify right ventricular volumes and ejection fraction compared to CMR in adult patients?
Effect estimate: R2=0.953
A novel attention-based deep learning method using 2D echocardiography can accurately quantify right ventricular volumes and ejection fraction, showing strong agreement with the cardiac MRI reference standard.
DL-based RV quantification on 2DE is feasible; leaves open prospective validation before clinical use.
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 (R 2 = .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.
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Kampaktsis et al. (2023) conducted an observational in Right ventricular quantification (n=50). Attention-based deep learning method (FT-Transformer) vs. Cardiac magnetic resonance imaging (CMR) was evaluated on Prediction of right ventricular volumes (R2=0.953). An attention-based deep learning method using 2D echocardiography achieved accurate prediction of right ventricular volumes (R2=0.953, absolute percentage error 9.75%) compared to cardiac magnetic resonance.
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