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Stephenson, N.* ; Pushparajah, K.* ; Wheeler, G.* ; Deng, S.* ; Schnabel, J.A. ; Simpson, J.M.*

Extended reality for procedural planning and guidance in structural heart disease - a review of the state-of-the-art.

Int. J. Cardiovasc. Imaging 39, 1405-1419 (2023)
DOI PMC
Creative Commons Lizenzvertrag
Extended reality (XR), which encompasses virtual, augmented and mixed reality, is an emerging medical imaging display platform which enables intuitive and immersive interaction in a three-dimensional space. This technology holds the potential to enhance understanding of complex spatial relationships when planning and guiding cardiac procedures in congenital and structural heart disease moving beyond conventional 2D and 3D image displays. A systematic review of the literature demonstrates a rapid increase in publications describing adoption of this technology. At least 33 XR systems have been described, with many demonstrating proof of concept, but with no specific mention of regulatory approval including some prospective studies. Validation remains limited, and true clinical benefit difficult to measure. This review describes and critically appraises the range of XR technologies and its applications for procedural planning and guidance in structural heart disease while discussing the challenges that need to be overcome in future studies to achieve safe and effective clinical adoption.
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Publication type Article: Journal article
Document type Review
Corresponding Author
Keywords Catheter Planning ; Extended Reality ; Procedure Guidance ; Structural Heart Disease ; Surgical Planning ; Virtual Reality; Virtual-reality; 3-dimensional Echocardiography; Augmented Reality; Beating-heart; Visualization; Display; Models; Feasibility; Navigation; Closure
ISSN (print) / ISBN 1569-5794
e-ISSN 1573-0743
Journal International Journal of Cardiovascular Imaging
Quellenangaben Volume: 39, Issue: 7, Pages: 1405-1419 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Dordrecht [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)