Validation of a novel software tool for automatic aortic annular sizing in three-dimensional transesophageal echocardiographic images

Background: Accurate aortic annulus (AoA) sizing is crucial for transcatheter aortic valve implantation planning. Three-dimensional (3D) transesophageal echocardiography (TEE) is a viable alternative to the standard multidetector row computed tomography (MDCT) for such assessment, with few automatic software solutions available. The aim of this study was to present and evaluate a novel software tool for automatic AoA sizing by 3D TEE.Methods: One hundred one patients who underwent both preoperative MDCT and 3D TEE were retrospectively analyzed using the software. The automatic software measurements' accuracy was compared against values obtained using standard manual MDCT, as well as against those obtained using manual 3D TEE, and intraobserver, interobserver, and test-retest reproducibility was assessed. Because the software can be used as a fully automatic or as an interactive tool, both options were addressed and contrasted. The impact of these measures on the recommended prosthesis size was then evaluated to assess if the software's automated sizes were concordant with those obtained using an MDCT- or a TEE-based manual sizing strategy.Results: The software showed very good agreement with manual values obtained using MDCT and 3D TEE, with the interactive approach having slightly narrower limits of agreement. The latter also had excellent intra- and interobserver variability. Both fully automatic and interactive analyses showed excellent test-retest reproducibility, with the first having a faster analysis time. Finally, either approach led to good sizing agreement against the true implanted sizes (> 77%) and against MDCT-based sizes (> 88%).Conclusion: Given the automated, reproducible, and fast nature of its analyses, the novel software tool pre- sented here may potentially facilitate and thus increase the use of 3D TEE for preoperative transcatheter aortic valve implantation sizing.This work was funded by projects "NORTE-01-0145-FEDER-000013'' and "NORTE-01-0145-FEDER-024300,'' supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund. This work has also been funded by European Regional Development Fund funds, through the Competitiveness Factors Operational Programme, and by national funds, through Fundacao para a Ciencia e Tecnologia, under the scope of the project POCI-01-0145-FEDER- 007038. The authors also acknowledge support from Fundacao para a Ciencia e Tecnologia and the European Social Found, through Programa Operacional Capital Humano, in the scope of PhD grants SFRH/BD/93443/2013 (S.Q.) and SFRH/BD/95438/2013 (P.M.).