Browsing by Author "Deakyne, Alex"
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Item Immersive Anatomical Scenes that Enable Multiple Users to Occupy the Same Virtual Space: A Tool for Surgical Planning and Education(2019-01) Deakyne, Alex3D modeling is becoming a well-developed field of medicine, but its applicability can be limited due to the lack of software allowing for easy utilizations of generated 3D visualizations. By leveraging recent advances in virtual reality, we can rapidly create immersive anatomical scenes as well as allow multiple users to occupy the same virtual space: i.e., over a local or distributed network. This setup is ideal for pre-surgical planning and education, allowing users to identify and study structures of interest. I demonstrate here such a pipeline on a broad spectrum of anatomical models and discuss its applicability to the medical field and its future prospects.Item The Uses of Artificial Intelligence and Virtual Reality Platforms for Developing the Next Generation of Anatomical, Medical Device, and Surgical Educational Tools(2021-04) Deakyne, AlexDetailed anatomical computational models offer many benefits for medical education and enhanced virtual reality platforms can be employed as tools to amplify these benefits. Primarily through its ability to preserve the 3D spatial information of the given anatomy while creating an immersive environment to learn. Further, many of the newly developed functionalities I developed and that are described in my dissertation allow for cost-effective collaborations and the novel abilities to perform computational deployments of devices within a detailed VR anatomic scene. While VR platforms offers developing alternatives to traditional anatomical and device learning, they require numerous detailed and verified computational anatomical models which can be difficult and time consuming to produce. As described within my thesis, recent advancements allow for AI assisted generations of anatomical computational models from medical image datasets. By combining these two technologies and platforms, our research team now has new abilities to create large anatomical computational 3D model datasets which can be used in Virtual Reality learning environments, enabling the next generation of anatomical, medical device, and surgical education tools.