Jahani, Bahar (2024) Mobile Augmented Reality Application For the Education of Neurovascular System. Masters thesis, Concordia University.
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Abstract
The human brain is a complex organ consisting of intricate vascular networks that supply local regions that control various aspects of our body functions. Understanding the vascular neuroanatomy and the associated territories is essential for diagnosing and treating cerebrovascular disorders such as stroke. Traditional anatomical education relies on 2D illustrations and physical dissections, which often fail to provide a comprehensive understanding of 3D anatomical structures. Augmented reality (AR) and virtual reality (VR) technologies offer immersive 3D visualizations, potentially enabling more effective learning experiences. In this thesis, we present NeuroVase, a tablet-based AR application designed to enhance stroke-related neuroanatomy learning. NeuroVase employs a novel dual-modal setup that leverages physical cue cards as the medium to interact with AR learning content and offer off-line textual learning materials. For the application, we developed a new learning curriculum for the cerebrovascular system with the care of stroke in mind. Users can inspect and explore the brain’s vascular territories, understand how blockages in major arteries can lead to specific types of strokes and symptoms, and access key knowledge summaries and 3D anatomical models and MRI. NeuroVase intends to address the limitations of traditional learning methods by offering a more interactive, engaging, and accessible learning tool. The included learning material in NeuroVase can potentially help support time-sensitive treatment decisions and improve prognosis assessment for cerebrovascular conditions, such as stroke. Our platform with modular learning materials was designed with flexibility in mind, making it easy to incorporate additional learning units as needed. Through our user study with 20 participants, the results suggest that NeuroVase could serve as a user-friendly and effective academic and clinical educational tool.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering |
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Item Type: | Thesis (Masters) |
Authors: | Jahani, Bahar |
Institution: | Concordia University |
Degree Name: | M. Comp. Sc. |
Program: | Computer Science |
Date: | August 2024 |
Thesis Supervisor(s): | Xiao, Yiming |
ID Code: | 994237 |
Deposited By: | Bahar Jahani |
Deposited On: | 24 Oct 2024 16:18 |
Last Modified: | 24 Oct 2024 16:18 |
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