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However, the gold standard for right ventricular quantification, cardiovascular magnetic resonance imaging (CMR), is not widely available, whereas widely used imaging techniques such as two-dimensional transthoracic echocardiography (2DE) are typically inaccurate due to the complex geometry of the right ventricle. Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of Right Ventricular Volume. This technology is an attention-based deep learning for cardiac right ventricular volume quantification using 2D echocardiography. 
