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Abstract
The number of transcatheter aortic valve implantation (TAVI) procedures is expected to increase significantly in the coming years. Improving efficiency will become essential for experienced operators performing large TAVI volumes, while new operators will require training and may benefit from accurate support. In this work, we present a fast deep learning method that can predict aortic annulus perimeter and area automatically from aortic annular plane images. We propose a method combining two deep convolutional neural networks followed by a postprocessing step. The models were trained with 355 patients using modern deep learning techniques, and the method was evaluated on another 118 patients. The method was validated against an interoperator variability study of the same 118 patients. The differences between the manually obtained aortic annulus measurements and the automatic predictions were similar to the differences between two independent observers (paired diff. of 3.3 ± 16.8 mm2 vs. 1.3 ± 21.1 mm2 for the area and a paired diff. of 0.6 ± 1.7 mm vs. 0.2 ± 2.5 mm for the perimeter). The area and perimeter were used to retrieve the suggested prosthesis sizes for the Edwards Sapien 3 and the Medtronic Evolut device retrospectively. The automatically obtained device size selections accorded well with the device sizes selected by operator 1. The total analysis time from aortic annular plane to prosthesis size was below one second. This study showed that automated TAVI device size selection using the proposed method is fast, accurate, and reproducible. Comparison with the interobserver variability has shown the reliability of the strategy, and embedding this tool based on deep learning in the preoperative planning routine has the potential to increase the efficiency while ensuring accuracy.
Highlights
Transcatheter aortic valve implantation (TAVI) has become the preferred treatment for patients with aortic stenosis at high risk for surgical aortic valve replacement (SAVR) [1]
multidetector computed tomography (MDCT) Imaging. is retrospective study used the anonymized data of 473 patients collected from multiple centra. e mean age of this cohort was 80.82 ± 7.18 years, and 55% of the patients were female. ere were 36 bicuspid patients in this cohort. e patient data consisted of volumetric MDCT images which were acquired to plan a TAVI procedure. erefore, all MDCT images were contrast-enhanced and contained a certain degree of aortic stenosis. e average row, column, and slice thickness of the MDCT images were 512.05 mm, 511.85 mm, and 0.83 mm. e aortic annular planes (AAP) were manually identified from the volumetric MDCT images using the standard method [7] and were used as input for this study
An automated method is proposed to facilitate and optimize the preoperative TAVI planning. It automatically predicts the area and perimeter of the aortic annulus based on MDCT images. e method has been validated on 118 patients to evaluate its accuracy, and the results show that the area and perimeter can be predicted in an automatic, reproducible, fast, and accurate way by combining the results of two networks followed by a postprocessing step. e differences between the manually obtained aortic annulus measurements and the automatic predictions are similar to the differences between two independent observers, which indicates a satisfying accuracy of the proposed approach. e area and perimeter have been used to retrieve the suggested prosthesis sizes for the Edwards sapien 3
Summary
Transcatheter aortic valve implantation (TAVI) has become the preferred treatment for patients with aortic stenosis at high risk for surgical aortic valve replacement (SAVR) [1]. Very recent clinical data even show that TAVI is at least as good as SAVR in low-risk patients [4, 5]. E number of TAVI procedures is increasing each year rapidly [6], and considering the recent clinical data for lowrisk patients will lead to an accelerated expansion in the coming years. Experienced operators can enlarge their volume of TAVI cases, for example, by increasing procedural e ciency. When focusing on the Journal of Interventional Cardiology preoperative planning, accurate automated detection of the aortic annulus dimensions directly from multidetector computed tomography (MDCT) images could increase efficiency and at the same time reduce operator variability, thereby minimizing the impact of experience on TAVI sizing
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