External Ventricular Drain Placement Using Active Augmented Reality Guidance: A Proof of Concept of a Clinically Integrable System.

Background And Objectives: Suboptimal placement occurs in 26% of external ventricular drain (EVD) procedures performed using traditional freehand methods. We developed a low-cost augmented reality stereotactic navigation system aimed at improving accuracy and safety of the procedure, which is readily compatible with existing Picture Archiving and Communication Systems and automated image segmentation algorithms.

Methods: The system integrates cloud storage, image segmentation, trajectory planning, point-based image-to-patient registration, and real-time 3-dimensional guidance superimposed over the surgical field.

Artificial Intelligence and Augmented Reality in Vascular Neurosurgery.

Background: The increasing adoption of artificial intelligence (AI) and augmented reality (AR) within vascular neurosurgery has become a prominent trend. The primary challenge before us is seamlessly integrating these advanced concepts and developing them further to improve patient outcomes.

Methods: We combined peer-reviewed publications of our research group over the past 5 years with current research projects to form the basis of a narrative discussion, aiming to better understand drawbacks, challenges, and the developmental steps to be followed.

Fully automatic anatomical landmark localization and trajectory planning for navigated external ventricular drain placement.

Objective: The aim of this study was to develop and validate a fully automatic anatomical landmark localization and trajectory planning method for external ventricular drain (EVD) placement using CT or MRI.

Methods: The authors used 125 preoperative CT and 137 contrast-enhanced T1-weighted MRI scans to generate 3D surface meshes of patients' skin and ventricular systems. Seven anatomical landmarks were manually annotated to train a neural network for automatic landmark localization.

Development and Validation of a Neurosurgical Phantom for Simulating External Ventricular Drain Placement.

This study aimed to develop and validate a cost-effective, customizable patient-specific phantom for simulating external ventricular drain placement, combining image segmentation, 3-D printing and molding techniques. Two variations of the phantom were created based on patient MRI data, integrating a realistic skin layer with anatomical landmarks, a 3-D printed skull, an agarose polysaccharide gel brain, and a ventricular cavity. To validate the phantom, 15 neurosurgeons, residents, and physician assistants performed 30 EVD placements.

Augmented Reality in Neurosurgery.

Augmented Reality (AR) involves superimposing digital content onto the real environment. AR has evolved into a viable tool in neurosurgery, enhancing intraoperative navigation, medical education and surgical training by integrating anatomical data with the real world. Neurosurgical AR relies on several key techniques to be successful, which includes image segmentation, model rendering, AR projection, and image-to-patient registration.

NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans.

Purpose: This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA).

Methods: We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA.

Fully automatic brain tumor segmentation for 3D evaluation in augmented reality.

Objective: For currently available augmented reality workflows, 3D models need to be created with manual or semiautomatic segmentation, which is a time-consuming process. The authors created an automatic segmentation algorithm that generates 3D models of skin, brain, ventricles, and contrast-enhancing tumor from a single T1-weighted MR sequence and embedded this model into an automatic workflow for 3D evaluation of anatomical structures with augmented reality in a cloud environment. In this study, the authors validate the accuracy and efficiency of this automatic segmentation algorithm for brain tumors and compared it with a manually segmented ground truth set.

Fully Automatic Adaptive Meshing Based Segmentation of the Ventricular System for Augmented Reality Visualization and Navigation.

Objective: Effective image segmentation of cerebral structures is fundamental to 3-dimensional techniques such as augmented reality. To be clinically viable, segmentation algorithms should be fully automatic and easily integrated in existing digital infrastructure. We created a fully automatic adaptive-meshing-based segmentation system for T1-weighted magnetic resonance images (MRI) to automatically segment the complete ventricular system, running in a cloud-based environment that can be accessed on an augmented reality device.

Current Accuracy of Augmented Reality Neuronavigation Systems: Systematic Review and Meta-Analysis.

Background: Augmented reality neuronavigation (ARN) systems can overlay three-dimensional anatomy and disease without the need for a two-dimensional external monitor. Accuracy is crucial for their clinical applicability. We performed a systematic review regarding the reported accuracy of ARN systems and compared them with the accuracy of conventional infrared neuronavigation (CIN).

Clinical Accuracy of Holographic Navigation Using Point-Based Registration on Augmented-Reality Glasses.

Background: As current augmented-reality (AR) smart glasses are self-contained, powerful computers that project 3-dimensional holograms that can maintain their position in physical space, they could theoretically be used as a low-cost, stand-alone neuronavigation system.

Objective: To determine feasibility and accuracy of holographic neuronavigation (HN) using AR smart glasses.

Methods: We programmed a fully functioning neuronavigation system on commercially available smart glasses (HoloLens®, Microsoft, Redmond, Washington) and tested its accuracy and feasibility in the operating room.

[Augmented reality glasses in the operating room].

Augmented reality is a technology that makes use of special glasses to combine various virtual images, such as holograms and scans, with reality. This technology offers important advantages for surgery in particular, because stereoscopic three-dimensional images of anatomical structures can be projected almost perfectly on the immobilised patient before and during surgery. This technology also has a lot of potential when it comes to education and providing information to patients.