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Title
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Enabling automated device size selection for transcatheter aortic valve implantation
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Author
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Abstract
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| 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 mm(2) vs. 1.3 +/- 21.1 mm(2) 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. |
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Language
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English
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Source (journal)
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Journal of interventional cardiology. - Mount Kisco, N.Y.
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Publication
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Mount Kisco, N.Y.
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2019
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ISSN
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0896-4327
1540-8183
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DOI
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10.1155/2019/3591314
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Volume/pages
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2019
(2019)
, 7 p.
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Article Reference
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UNSP 3591314
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ISI
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000499983000004
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Pubmed ID
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31777469
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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