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Abstract Aims Mitral transcatheter edge‐to‐edge repair (M‐TEER) is a valid treatment option for severe mitral regurgitation (MR), necessitating accurate risk stratification of M‐TEER candidates for effective patient selection, optimal periprocedural care and improved long‐term outcomes. The body mass index (BMI) is a simple and practical prognostic index, and the obesity paradox has been widely reported. Methods and Results Between April 2018 and June 2021, 2149 patients undergoing M‐TEER were registered in the prospective multicentre registry and classified into three groups: underweight (BMI

Background and objective: Information on the location of the fossa ovalis (FO) is necessary for planning interventional procedures that require an inter-atrial septal (IAS) puncture. At present, this information is obtained manually from pre-procedural medical images, which is time consuming with limited reproducibility. In this paper, we propose a method to automatically segment the FO region from computed tomography (CT) images. Methods: Our proposed method roughly crops CT images based on atlas information of the FO and heart chambers, and inputs the cropped CT images to a U-Net-based deep neural network (DNN) to segment the FO region. This method was evaluated by five-fold cross validation using 215 CT images with manually annotated FO regions, and its segmentation accuracy was compared to two previously reported methods based on thinness of the IAS wall and on simple DNN. Results: The segmentation process was successful in all cases for the IAS-based method, but failed in 4 cases for the proposed method and in 30 cases for the DNN method due to irregular heart structure. For the segmentation accuracies of our proposed method, the IAS wall thinness-based method and DNN based-method, mean chamfer distances were 2.16 ± 1.43, 3.30 ± 1.37 and 2.66 ± 1.32 respectively, with the difference being statistically significant. Conclusions: These results suggest that our proposed method can automatically segment the FO region more accurately with fewer failures.