Breakthrough and Challenging Application: Mixed Reality-Assisted Intracardiac Surgery
Background: While several studies investigate the utility and clinical value of 3D printing in aiding diagnosis, medical education, preoperative planning, and intraoperative guidance of surgical interventions, there is a scarcity of literature regarding concrete applications of mixed reality in the cardiovascular domain due to its nascent stage of study and expansion. This study goes beyond a mere three-dimensional visualization of the cardiac district, aiming to visualize the intracardiac structures within the scope of preoperative planning for cardiac surgery. Methods: The segmentation of the heart was performed through an open-source and a professional software and by applying different procedures. Each anatomical component of the heart, including the aortic valve, was accurately segmented and a 3D model was built to represent the entire heart. Results: Beyond the three-dimensional visualization of the cardiac region, the intracardiac structures were also segmented. A mixed-reality app was implemented with the possibility of exploding the model, interacting with it, and freely sectioning it with a plane. Conclusions: The proposed segmentation methodology allows a segmentation of the valve and the intracardiac structures. Furthermore, the mixed-reality app has confirmed the potential of this technology in diagnostic and preoperative planning, although some limitations should still be overcome.
Authors
- Mizar D’Abramo (ORCID: https://orcid.org/0000-0002-7310-7410)
- Wael Saade (ORCID: https://orcid.org/0000-0001-6617-6103)
- Franco Marinozzi (ORCID: https://orcid.org/0000-0002-4872-2980)
- Michela Franzò (ORCID: https://orcid.org/0000-0001-6955-7534)
- Fabiano Bini (ORCID: https://orcid.org/0000-0002-5641-1189)
- Giusseppe Mazzesi (ORCID: https://orcid.org/0000-0003-0227-7233)
- Sara Bicchierini
Institutions
- Policlinico Umberto I (IT)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- Applied Sciences
- Published
- 2024-11-06
- DOI
- https://doi.org/10.3390/app142210151
- Citations
- 1
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.57