Abstract
The integration of mixed reality (MR) and 3D printing technologies is revolutionizing cardiac education by offering immersive and interactive learning experiences that were previously unattainable. This chapter explores the deployment of these technologies within educational platforms specifically designed for cardiac training. Utilizing Virtual Reality (VR) systems enhanced with anaglyph 3D capabilities, are simple but effective methods for viewing stereoscopic 3D images: educators can forge more accessible and collaborative learning environments. These advancements significantly improve one's anatomical understanding as well as provide for novel procedural training. Generated computational 3D models, derived from high-resolution perfusion-fixed human cardiac specimens, enable precise virtual placements of medical devices. These placements in turn enhance the design process and directly improve educational outcomes by allowing for accurate procedural simulations. Furthermore, this chapter discusses the challenges and advances in medical imaging analyses, highlighting the applications of convolutional neural networks (CNNs) for automated cardiac volume segmentations. This technology supports the creation of detailed 3D cardiac models, vital for pre-surgical planning and educational applications. The synergistic use of MR and 3D printing not only transforms the pedagogical approach to cardiac education but also boosts the efficacies of both medical training and patient care.
Original language | English (US) |
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Title of host publication | Handbook of Cardiac Anatomy, Physiology, and Devices |
Subtitle of host publication | Fourth Edition |
Publisher | Springer Nature |
Pages | 967-981 |
Number of pages | 15 |
ISBN (Electronic) | 9783031725814 |
ISBN (Print) | 9783031725807 |
DOIs | |
State | Published - Dec 8 2024 |
Bibliographical note
Publisher Copyright:© The Author(s), 2024. All rights reserved.
Keywords
- 3D printing
- Amplatzer device
- Augmented reality (AR)
- Cardiac education
- Computational modeling
- Convolutional neural networks (CNNs)
- Deep learning
- Interdisciplinary collaboration
- Medical device design
- Medical imaging analysis
- Medical simulators
- Mixed reality (MR)
- Regulatory pathways
- Seldinger technique
- Stenting simulation
- Transesophageal echocardiography (TEE)
- Transthoracic echocardiography (TTE)
- Virtual prototyping
- Virtual reality (VR)