Recent advancements in computer vision, enhanced augmented reality (AR) software and hardware are paving the way for AR to emerge as a promising and innovative tool for advancing and broadening neurosurgical pre-operative planning and intraoperative procedures. In this study, we describe the use of AR coupled with Magnetic Resonance (MR) and Diffusion Tensor Imaging (DTI) to map visual tracts around an occipital lesion, ensuring a safe resection without post-op complications. In a patient who underwent an elective craniotomy for the resection of an occipital brain tumor, preoperative MRI and Quicktome platform-based DTI MRI were uploaded onto the Medivis AR system. Pre-op and post-op fiber tractography was conducted using DSI Studio, and fiber volumes were compared. This technological setup was employed for pre-operative and intraoperative planning and further enhanced clinical decision-making for the actual tumor resection. The AR system and the preparation process are demonstrated via an illustrative video. The visual pathways were preserved during surgery consistent with the intraoperative visualization of these tracks on DTI using an AR headset. Clinically, postoperative visual field testing revealed no signs of visual loss. The volumes of the visual tracts were measured in preoperative and postoperative DTIs, showing that their volume was maintained. Our study highlights the feasibility in identifying preoperative tumor borders for incision planning and feasibility in preserving white matter tracts during tumor resection. AR FT-assisted surgery is safe and effective in planning the surgical approach for patients with brain tumors, thereby optimizing patient functional outcomes.
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http://dx.doi.org/10.1016/j.clineuro.2025.108820 | DOI Listing |
IEEE Trans Vis Comput Graph
March 2025
In Augmented Reality (AR), virtual content enhances user experience by providing additional information. However, improperly positioned or designed virtual content can be detrimental to task performance, as it can impair users' ability to accurately interpret real-world information. In this paper we examine two types of task-detrimental virtual content: obstruction attacks, in which virtual content prevents users from seeing real-world objects, and information manipulation attacks, in which virtual content interferes with users' ability to accurately interpret real-world information.
View Article and Find Full Text PDFJAMA Surg
March 2025
Division of Plastic Surgery, Department of Surgery, The University of British Columbia, Vancouver, British Columbia, Canada.
Br J Oral Maxillofac Surg
February 2025
Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. Electronic address:
In the last decades, maxillomandibular reconstruction has been revolutionised by the use of free flaps and virtual surgical planning technologies. However, the currently available applied physical cutting guides provide no intraoperative flexibility, and adjustments based on intraoperative findings are not possible. A novel augmented reality (AR)-guided technique is presented that allows for quick intraoperative surgical planning adaptations.
View Article and Find Full Text PDFClin Exp Optom
March 2025
Department of Optometry and Vision Science, University of Latvia, Riga, Latvia.
Clinical Relevance: Headset-based simulator training is increasingly utilised in eye care education, offering opportunities to improve clinical skills in a controlled, reproducible environment. These tools support the development of innovative training approaches in eye care.
Background: While the educational advantages of headset-based simulators are recognised, the potential challenges and limitations that users may encounter remain understudied.
Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation.
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