Deep learning enables fully automated cineCT-based assessment of regional right ventricular function.

Aims: Right ventricular (RV) function is a key factor in the diagnosis and prognosis of heart disease. However, current advanced computed tomography (CT)-based assessments rely on semi-automated segmentation of the RV blood pool and manual delineation of the RV free and septal wall boundaries. These steps are time-consuming and prone to inter- and intra-observer variability. Methods and results: We developed and evaluated a fully automated pipeline consisting of two deep learning methods to automate volumetric and regional strain analysis of the RV from contrast-enhanced, electrocardiogram (ECG)-gated cineCT images. The Right Heart Blood Segmenter (RHBS) is a 3D high-resolution configuration of nnU-Net to define the endocardial boundary, while the Right Ventricular Wall Labeler (RVWL) is a 3D point cloud-based deep learning method to label the free and septal walls. We trained our models using a diverse cohort of patients with different RV phenotypes and tested them in an independent cohort of patients with aortic stenosis undergoing TAVR. Our approach demonstrated high accuracy in both cross-validation and independent validation cohorts. RHBS and RVWL both yielded Dice scores of 0.96 and accurate volumetry metrics. RVWL achieved high Dice scores (>0.90) and high accuracy (>93%) for wall labelling. The combination of RHBS + RVWL provided an accurate assessment of free and septal wall regional strain, with a median cosine similarity value of 0.97 in the independent cohort. Conclusion: A fully automated 3D cineCT-based RV regional strain analysis pipeline has the potential to significantly enhance the efficiency and reproducibility of RV function assessment, enabling the evaluation of large cohorts and multi-centre studies.