Peripheral intravenous catheter (PIVC) insertion is a common yet challenging procedure. Although ultrasound guidance improves procedural accuracy and patient outcome, its complexity limits its routine adoption to highly experienced clinicians. This paper introduces a virtual reality (VR) simulator developed specifically for training in ultrasound-guided PIVC insertions. This study aims to validate the simulator's realism and relevance through face, content, and construct assessments, and to demonstrate its utility as a platform for comparing various approaches to PIVC insertion. Thirty participants from diverse medical backgrounds and levels of expertise completed three scenarios, each featuring a different procedural technique, within the simulator's controlled virtual environment. The simulator demonstrated strong face and content validity, with participants rating its realism at 7.1/10 and enjoyment at 8.2/10. Performance data showed that expert participants maintained higher success rates and performance across all procedural scenarios, supporting the simulator's construct validity. In the standard approach scenario, novices required 230.91 ± 158.77 s to complete the task and achieved only a 45% success rate compared to experts' 95.48 ± 65.74 s and 80% success rate. In the procedural scenario involving an alignment assistance device, where needle insertion was aligned with the ultrasound image plane, novice success rates increased to 75% and the number of attempts decreased from 8.95 ± 6.69 to 2.75 ± 2.67, narrowing the performance gap with experts. These findings highlight the simulator's potential not only as an effective training tool but also as a platform for the objective evaluation of different procedural techniques.

In the past decade, virtual reality (VR) and augmented reality (AR) have seen a second wave of activity due to consumer-level availability of devices. In the medical realm, use of VR and AR has been investigated extensively and successfully for training, teaching, rehabilitation, and therapy. However, extended reality (XR) and its intraoperative use in the operating room has yet been underexplored and underdeveloped. Intelligent XR environments that integrate advanced visualization, intuitive interaction, and AI-assisted decision support have the potential to transform clinical care. Responsive, context-aware spaces could enhance medical teams’ capabilities by enabling real-time access to critical information, enhancing situation awareness, and supporting remote collaboration and access to clinical expertise across geographical boundaries—all while preserving the natural flow of procedures. But to realize the potential benefits of XR in the operating room requires a significant and focused effort by an interdisciplinary community. It is the intention of this paper to help to catalyze such an effort. In this paper, we identify pressing issues in clinical care, the potential of XR to address them, and the further research that is needed to realize that potential. We look at a broad range of areas where XR can play an important role, including direct support for the surgeon, support for the surgical team, support for the patient, and remote collaboration. We also consider ways in which XR can be integrated with AI to provide enhanced information and interaction. Discussions are underpinned by considerations of methods to evaluate contributions and progress. This paper is the result of a Dagstuhl Seminar on Extended Reality for the Operating Room (XR4OR), held at the Leibniz Center for Informatics, Schloss Dagstuhl, Germany, in February 2025. It gathered twenty-four researchers working in the areas of virtual reality, augmented reality, medical informatics, human-computer interaction, and surgery.

Image-guided neurosurgery demands precise depth perception to minimize cognitive burden during intricate navigational tasks. Existing evaluation methods rely heavily on subjective user feedback, which can be biased and inconsistent. This study uses a physiological measure via electroencephalography (EEG), to quantify cognitive load when using novel dynamic depth‐cue visualizations. By comparing dynamic versus static rendering techniques, we aim to establish an objective framework for assessing and validating visualization strategies beyond traditional performance metrics. Twenty participants (experts in brain imaging) navigated to specified targets within a computed tomography angiography (CTA) volume using a tracked 3D pointer. We implemented three visualization methods (shading, ChromaDepth, aerial perspective) in both static and dynamic modes, randomized across 80 trials per subject. Continuous EEG was recorded via a Muse headband; raw signals were preprocessed and theta‐band (4–7 Hz) power extracted for each trial. A two‐way repeated measures ANOVA assessed the effects of visualization type and dynamic interaction on theta power. Dynamic visualization conditions yielded lower mean theta‐band power compared to static conditions (Δ = 0.057 V2/Hz; F (1,19) = 6.00, p = 0.024), indicating reduced neural markers of cognitive load. No significant main effect was observed across visualization methods, nor their interaction with dynamic mode. These findings suggest that real‐time feedback from pointer‐driven interactions may alleviate mental effort regardless of the specific depth cue employed. Our exploratory results demonstrate the feasibility of using consumer‐grade EEG to provide objective insights into cognitive load for surgical visualization techniques. Although limited by non‐surgeon participants, the observed theta‐power reductions under dynamic conditions support further investigation. Future work should correlate EEG‐derived load measures with performance outcomes, involve practising neurosurgeons, and leverage high-density EEG or AI‐driven adaptive visualization to refine and validate these preliminary findings.

Virtual reality (VR) facilitates immersive visualization and interaction with complex medical images in immersive platforms. It remains unclear which VR input schemes and devices are optimal for these tasks. In this study, we perform a 12-person study to investigate user performance and experience while performing medical image segmentation with two control schemes, keyboard and mouse (KBM) and VR motion controllers (MC). Our results showed that motion controllers are faster in smaller segmentation tasks and offer a more pleasant user experience, however no significant difference in user accuracy was observed.

PURPOSE:Surgical planning is essential before the surgery, especially for spinal tumors resection. During the surgical planning, medical images are analyzed by surgeon on a standardized display mode using a computer. But this display mode has its limit in term of spatial perception of the anatomical structures. Our purpose in this study is to assess the impact of using another display mode like virtual reality (VR) on the surgical planning of spinal tumors resection by comparing VR with conventional computer-based visualization. METHODS:A user study was conducted with eight neurosurgeons, who planned six spinal tumor surgeries using both VR and computer visualization modalities. The evaluation focused on the perception of anatomical-functional information from medical images, the identification of anatomical structures, and the selection of surgical approaches represented by the number of anatomical structures traversed to attend the tumor. These parameters were assessed using objective questionnaires developed from a work domain analysis (WDA) already proved in brain surgery. We then adapted the WDA to spinal surgery. RESULTS:VR made it easier to perceive a greater number of anatomical-functional information compared to computer visualization. Surgeons identified a greater anatomical structure with VR compared to computer visualization. Furthermore, surgeons selected additional anatomical structures to be traversed to reach the tumor when using VR, leading to a more precise selection of surgical approaches. These findings can predict the added value of VR in helping surgical decision-making when planning surgery. CONCLUSION:VR can be a promising tool for surgical planning by providing an immersive and interactive perspective that enhances understanding of anatomy. However, our finding is from an exploratory study, more clinical cases should be conducted to demonstrate its feasibility and reliability.

Virtual reality (VR) can offer immersive platforms for segmenting complex medical images to facilitate a better understanding of anatomical structures for training, diagnosis, surgical planning, and treatment evaluation. These applications rely on user interaction within the VR environment to manipulate and interpret medical data. However, the optimal interaction schemes and input devices for segmentation tasks in VR remain unclear. This study compares user performance and experience using two different input schemes. Twelve participants segmented 6 CT/MRI images using two input methods: keyboard and mouse (KBM) and motion controllers (MCs). Performance was assessed using accuracy, completion time, and efficiency. A post-task questionnaire measured users’ perceived performance and experience. No significant overall time difference was observed between the two input methods, though KBM was faster for larger segmentation tasks. Accuracy was consistent across input schemes. Participants rated both methods as equally challenging, with similar efficiency levels, but found MCs more enjoyable to use. These findings suggest that VR segmentation software should support flexible input options tailored to task complexity. Future work should explore enhancements to motion controller interfaces to improve usability and user experience.

Visualizing 3D medical images is challenging due to self-occlusion, where anatomical structures of interest can be obscured by surrounding tissues. Existing methods, such as slicing and interactive clipping, are limited in their ability to fully represent internal anatomy in context. In contrast, hand-drawn medical illustrations in anatomy books manage occlusion effectively by selectively removing portions based on tissue type, revealing 3D structures while preserving context. This paper introduces AnatomyCarve, a novel technique developed for a VR environment that creates high-quality illustrations similar to those in anatomy books, while remaining fast and interactive. AnatomyCarve allows users to clip selected segments from 3D medical volumes, preserving spatial relations and contextual information. This approach enhances visualization by combining advanced rendering techniques with natural user interactions in VR. Usability of AnatomyCarve was assessed through a study with non-experts, while surgical planning effectiveness was evaluated with practicing neurosurgeons and residents. The results show that AnatomyCarve enables customized anatomical visualizations, with high user satisfaction, suggesting its potential for educational and clinical applications.

Multivariable regression analysis for the Moderate-severe anxiety, whole cohort.

PLINK results for the Moderate-severe depression.

Multivariable regression analysis for the Trail making test, female participants.

Multivariable regression analysis for the Moderate-severe anxiety, male participants.

Manhattan plot for the Trail making test

In this paper, we present a new volumetric ambient occlusion algorithm called Contextual Ambient Occlusion (CAO) that supports real-time clipping. The algorithm produces ambient occlusion images of exactly the same quality as Local Ambient Occlusion (LAO) while enabling real-time modification to the shape used to clip the volume. The main idea of the algorithm is that clipping only affects the ambient value of a small number of voxels, so by identifying these voxels and recalculating the ambient factor only for them, it is possible to significantly increase the rendering performance (by 2-5x) without decreasing the quality of the rendered image. Due to its fast performance, the algorithm is suitable for interactive environments where clipping changes could occur every frame. Additionally, the algorithm does not have any stereoscopic inconsistency, which makes it suitable for mixed reality environments. This paper is an extended version of the “Contextual Ambient Occlusion” article presented during the 2023 Graphics Interface conference, and includes, among other additions, the source code of the algorithm.

Multivariable regression analysis for the Trail making test, male participants.

Functional description of associations.

Additional analyses from the SJLIFE Cohort.

3D echocardiography (3DE) is the standard modality for visualizing heart valves and their surrounding anatomical structures. Commercial cardiovascular ultrasound systems commonly offer a set of parameters that allow clinical users to modify, in real time, visual aspects of the information contained in the echocardiogram. To our knowledge, there is currently no work that demonstrates if the methods currently used by commercial platforms are optimal. In addition, current platforms have limitations in adjusting the visibility of anatomical structures, such as reducing information that obstructs anatomical structures without removing essential clinical information. To overcome this, the present work proposes a new method for 3DE visualization based on "focus + context" (F+C), a concept which aims to present a detailed region of interest while preserving a less detailed overview of the surrounding context. The new method is intended to allow clinical users to modify parameter values differently within a certain region of interest, independently from the adjustment of contextual information. To validate this new method, a user study was conducted amongst clinical experts. As part of the user study, clinical experts adjusted parameters for five echocardiograms of patients with complete atrioventricular canal defect (CAVC) using both the method conventionally used by commercial platforms and the proposed method based on F+C. The results showed relevance for the F+C-based method to visualize 3DE of CAVC patients, where users chose significantly different parameter values with the F+C-based method.

PLINK results for the Moderate-severe anxiety.