The future of telerehabilitation: embracing virtual reality and augmented reality innovations.

The integration of virtual reality (VR) and augmented reality (AR) into the telerehabilitation initiates a major change in the healthcare practice particularly in neurological and also orthopedic rehabilitation. This essay reflects the potential of the VR and AR in their capacity to create immersive, interactive environments that facilitate the recovery. The recent developments have illustrated the ability to enhance the patient engagement and outcomes, especially in tackling the complex motor and cognitive rehabilitation needs. The combination of artificial intelligence (AI) with VR and AR will bring the rehabilitation to the next level by enabling adaptive and responsive treatment programs provided through real-time feedback and predictive analytics. Nevertheless, the issues such as availability, cost, and digital gap among many others present huge obstacles to the mass adoption. This essay provides a very thorough review of the existing level of virtual reality and augmented reality in rehabilitation and examines the many potential gains, drawbacks, and future directions from a different perspective.

Holographic mixed reality for planning transcatheter aortic valve replacement.

BACKGROUND: Using three-dimensional (3D) modalities for optimal pre-procedure planning in transcatheter aortic valve replacement (TAVR) is critical for procedural success. However, current methods rely on visualizing images on a two-dimensional screen, using shading and colors to create the illusion of 3D, potentially impeding the accurate comprehension of the actual anatomy structures. In contrast, a new Mixed Reality (MxR) based software enables accurate 3D visualization, imaging manipulation, and quantification of measurements. AIMS: The study aims to evaluate the feasibility, reproducibility, and accuracy of dimensions of the aortic valve complex as measured with a new holographic MxR software (ARTICOR®, Artiness srl, Milano, Italy) compared to a widely used software for pre-operative sizing and planning (3mensio Medical Imaging BV, Bilthoven, The Netherlands). METHODS: This retrospective, observational, double-center study enrolled 100 patients with severe aortic stenosis who underwent cardiac computed tomography (CCT) before TAVR. The CCT datasets of volumetric aortic valve images were analyzed using 3Mensio and newly introduced MxR-based software. RESULTS: Ninety-eight percent of the CCT datasets were successfully converted into holographic models. A higher level of agreement between the two software systems was observed for linear metrics (short, long, and average diameter). In comparison, agreement was lower for area, perimeter, and annulus-to-coronary ostia distance measurements. Notably, the annulus area, annular perimeter, left ventricular outflow tract (LVOT) area, and LVOT perimeter were significantly and consistently smaller with the MxR-based software compared to the 3Mensio. Excellent interobserver reliability was demonstrated for most measurements, especially for direct linear measurements. CONCLUSIONS: Linear measurements of the aortic valve complex using MxR-based software are reproducible compared to the standard CCT dataset analyzed with 3Mensio. MxR-based software could represent an accurate tool for the pre-procedural planning of TAVR.

Physical loads on upper extremity muscles while interacting with virtual objects in an augmented reality context.

Augmented reality (AR) environments are emerging as prominent user interfaces and gathering significant attention. However, the associated physical strain on the users presents a considerable challenge. Within this background, this study explores the impact of movement distance (MD) and target-to-user distance (TTU) on the physical load during drag-and-drop (DND) tasks in an AR environment. To address this objective, a user experiment was conducted utilizing a 5× 5 within-subject design with MD (16, 32, 48, 64, and 80 cm) and TTU (40, 80, 120, 160, and 200 cm) as the variables. Physical load was assessed using normalized electromyography (NEMG) (%MVC) indicators of the upper extremity muscles and the physical item of NASA-Task load index (TLX). The results revealed significant variations in the physical load based on MD and TTU. Specifically, both the NEMG and subjective physical workload values increased with increasing MD. Moreover, NEMG increased with decreasing TTU, whereas the subjective physical workload scores increased with increasing TTU. Interaction effects of MD and TTU on NEMG were also significantly observed. These findings suggest that considering the MD and TTU when developing content for interacting with AR objects in AR environments could potentially alleviate user load.

Convex-based lightweight feature descriptor for Augmented Reality Tracking.

Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. We have developed two filters capable of computing pixel intensity variations, followed by the covariance matrix of the polynomial to describe the features. The superiority of CBFD is validated through precision, recall, computation time, and feature location distance. Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. Noteworthy is CBFD's recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. Finally, the plot of location feature distance illustrates that CBFD exhibits minimal distance compared to DITF and Histogram of Oriented Gradients (HOG). These results highlight the speed and robustness of CBFD across various transformations.

Determination of rotation center and diameter of femoral heads using off-the-shelf augmented reality hardware for navigation.

In total hip arthroplasty (THA), determining the center of rotation (COR) and diameter of the hip joint (acetabulum and femoral head) is essential to restore patient biomechanics. This study investigates on-the-fly determination of hip COR and size, using off-the-shelf augmented reality (AR) hardware. An AR head-mounted device (HMD) was configured with inside-out infrared tracking enabling the determination of surface coordinates using a handheld stylus. Two investigators examined 10 prosthetic femoral heads and cups, and 10 human femurs. The HMD calculated the diameter and COR through sphere fitting. Results were compared to data obtained from either verified prosthetic geometry or post-hoc CT analysis. Repeated single-observer measurements showed a mean diameter error of 0.63 mm ± 0.48 mm for the prosthetic heads and 0.54 mm ± 0.39 mm for the cups. Inter-observer comparison yielded mean diameter errors of 0.28 mm ± 0.71 mm and 1.82 mm ± 1.42 mm for the heads and cups, respectively. Cadaver testing found a mean COR error of 3.09 mm ± 1.18 mm and a diameter error of 1.10 mm ± 0.90 mm. Intra- and inter-observer reliability averaged below 2 mm. AR-based surface mapping using HMD proved accurate and reliable in determining the diameter of THA components with promise in identifying COR and diameter of osteoarthritic femoral heads.

Principles of digital professionalism for the metaverse in healthcare.

BACKGROUND: Experts are currently investigating the potential applications of the metaverse in healthcare. The metaverse, a groundbreaking concept that arose in the early 21st century through the fusion of virtual reality and augmented reality technologies, holds promise for transforming healthcare delivery. Alongside its implementation, the issue of digital professionalism in healthcare must be addressed. Digital professionalism refers to the knowledge and skills required by healthcare specialists to navigate digital technologies effectively and ethically. This study aims to identify the core principles of digital professionalism for the use of metaverse in healthcare. METHOD: This study utilized a qualitative design and collected data through semi-structured online interviews with 20 medical information and health informatics specialists from various countries (USA, UK, Sweden, Netherlands, Poland, Romania, Italy, Iran). Data analysis was conducted using the open coding method, wherein concepts (codes) related to the themes of digital professionalism for the metaverse in healthcare were assigned to the data. The analysis was performed using the MAXQDA software (VER BI GmbH, Berlin, Germany). RESULTS: The study revealed ten fundamental principles of digital professionalism for the metaverse in healthcare: Privacy and Security, Informed Consent, Trust and Integrity, Accessibility and Inclusion, Professional Boundaries, Evidence-Based Practice, Continuous Education and Training, Collaboration and Interoperability, Feedback and Improvement, and Regulatory Compliance. CONCLUSION: As the metaverse continues to expand and integrate itself into various industries, including healthcare, it becomes vital to establish principles of digital professionalism to ensure ethical and responsible practices. Healthcare professionals can uphold these principles to maintain ethical standards, safeguard patient privacy, and deliver effective care within the metaverse.

Robot-Assisted Augmented Reality (AR)-Guided Surgical Navigation for Periacetabular Osteotomy.

Periacetabular osteotomy (PAO) is an effective approach for the surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip joint and the limited field of view (FoV) during the surgery, it is challenging for surgeons to perform a PAO surgery. To solve this challenge, we propose a robot-assisted, augmented reality (AR)-guided surgical navigation system for PAO. The system mainly consists of a robot arm, an optical tracker, and a Microsoft HoloLens 2 headset, which is a state-of-the-art (SOTA) optical see-through (OST) head-mounted display (HMD). For AR guidance, we propose an optical marker-based AR registration method to estimate a transformation from the optical tracker coordinate system (COS) to the virtual space COS such that the virtual models can be superimposed on the corresponding physical counterparts. Furthermore, to guide the osteotomy, the developed system automatically aligns a bone saw with osteotomy planes planned in preoperative images. Then, it provides surgeons with not only virtual constraints to restrict movement of the bone saw but also AR guidance for visual feedback without sight diversion, leading to higher surgical accuracy and improved surgical safety. Comprehensive experiments were conducted to evaluate both the AR registration accuracy and osteotomy accuracy of the developed navigation system. The proposed AR registration method achieved an average mean absolute distance error (mADE) of 1.96 ± 0.43 mm. The robotic system achieved an average center translation error of 0.96 ± 0.23 mm, an average maximum distance of 1.31 ± 0.20 mm, and an average angular deviation of 3.77 ± 0.85°. Experimental results demonstrated both the AR registration accuracy and the osteotomy accuracy of the developed system.

Effectiveness of virtual and augmented reality for cardiopulmonary resuscitation training: a systematic review and meta-analysis.

BACKGROUND: Virtual reality (VR) and augmented reality (AR) are emerging technologies that can be used for cardiopulmonary resuscitation (CPR) training. Compared to traditional face-to-face training, VR/AR-based training has the potential to reach a wider audience, but there is debate regarding its effectiveness in improving CPR quality. Therefore, we conducted a meta-analysis to assess the effectiveness of VR/AR training compared with face-to-face training. METHODS: We searched PubMed, Embase, Cochrane Library, Web of Science, CINAHL, China National Knowledge Infrastructure, and Wanfang databases from the inception of these databases up until December 1, 2023, for randomized controlled trials (RCTs) comparing VR- and AR-based CPR training to traditional face-to-face training. Cochrane's tool for assessing bias in RCTs was used to assess the methodological quality of the included studies. We pooled the data using a random-effects model with Review Manager 5.4, and assessed publication bias with Stata 11.0. RESULTS: Nine RCTs (involving 855 participants) were included, of which three were of low risk of bias. Meta-analyses showed no significant differences between VR/AR-based CPR training and face-to-face CPR training in terms of chest compression depth (mean difference [MD], -0.66 mm; 95% confidence interval [CI], -6.34 to 5.02 mm; P = 0.82), chest compression rate (MD, 3.60 compressions per minute; 95% CI, -1.21 to 8.41 compressions per minute; P = 0.14), overall CPR performance score (standardized mean difference, -0.05; 95% CI, -0.93 to 0.83; P = 0.91), as well as the proportion of participants meeting CPR depth criteria (risk ratio [RR], 0.79; 95% CI, 0.53 to 1.18; P = 0.26) and rate criteria (RR, 0.99; 95% CI, 0.72 to 1.35; P = 0.93). The Egger regression test showed no evidence of publication bias. CONCLUSIONS: Our study showed evidence that VR/AR-based training was as effective as traditional face-to-face CPR training. Nevertheless, there was substantial heterogeneity among the included studies, which reduced confidence in the findings. Future studies need to establish standardized VR/AR-based CPR training protocols, evaluate the cost-effectiveness of this approach, and assess its impact on actual CPR performance in real-life scenarios and patient outcomes. TRIAL REGISTRATION: CRD42023482286.

Scientometric Analysis of the World Scientific Production on Augmented and Virtual Reality in Dental Education.

AIM: The aim of this study was to perform a comprehensive bibliometric analysis of virtual reality (VR) and augmented reality (AR) applications in dental education. MATERIALS AND METHODS: A cross-sectional research was carried out using a bibliometric methodology. This process entailed the assessment of metadata from scientific publications that are catalogued in the Scopus database, covering the period from January 2018 to August 2023. A variety of indicators were utilized to scrutinize scientific production and dissemination within the academic community. These encompassed elements such as the author, the publication itself, the number of citations, institutional and collaborative affiliations, geographical location, journal quartile ranking, h-index, Source Normalized Impact per Paper (SNIP), Field-Weighted Citation Impact (FWCI), SCImago Journal Rank (SJR), and the CiteScore. RESULTS: Several institutions from different countries and their academic output were found. Beihang University stands out with 16 scholarly articles, followed by Stanford University with 16 articles and 170 citations. The Q1 quartile has experienced a steady increase, reaching 87 scientific articles. The top 10 authors in scientific production on augmented and VR in dentistry include Joe Amal Cecil, Avinash Gupta, and Miguel A Pirela-Cruz. In terms of co-authorship by country, the United States, Germany, and China are the most predominant in the clusters represented. However, other clusters also have a significant presence. By analyzing the explored trends and themes of keyword co-occurrence, four main clusters were identified. The yellow cluster contained the largest amount of research with the keyword "virtual reality." In addition, the blue cluster was found to be best related to the green "simulation," purple "virtual reality (VR)," and light blue "human-centered computing" clusters. CONCLUSION: This study evidenced the availability and quality of the data used for the analysis. Future studies could consider the use of VR systems with integrated eye tracking and compare their effect in dentistry during dental procedures. CLINICAL SIGNIFICANCE: The clinical importance of this study lies in its potential to improve dental education. The VR and AR can provide dental students with immersive, hands-on learning experiences, which can enhance their understanding and clinical skills. Furthermore, the translational value of this study extends beyond dental education. The insights gained from this research could be applicable to other fields of medical education where hands-on training is crucial. Thus, the findings of this study have the potential to influence the broader landscape of medical education, ultimately leading to improved healthcare outcomes. How to cite this article: Alvitez-Temoche D, Silva H, Aguila ED, et al. Scientometric Analysis of the World Scientific Production on Augmented and Virtual Reality in Dental Education. J Contemp Dent Pract 2024;25(4):358-364.

Characterizing the Effects of Adding Virtual and Augmented Reality in Robot-Assisted Training.

Extended reality (XR) technology combines physical reality with computer synthetic virtuality to deliver immersive experience to users. Virtual reality (VR) and augmented reality (AR) are two subdomains within XR with different immersion levels. Both of these have the potential to be combined with robot-assisted training protocols to maximize postural control improvement. In this study, we conducted a randomized control experiment with sixty-three healthy subjects to compare the effectiveness of robot-assisted posture training combined with VR or AR against robotic training alone. A robotic Trunk Support Trainer (TruST) was employed to deliver assistive force at the trunk as subjects moved beyond the stability limits during training. Our results showed that both VR and AR significantly enhanced the training outcomes of the TruST intervention. However, the VR group experienced higher simulator sickness compared to the AR group, suggesting that AR is better suited for sitting posture training in conjunction with TruST intervention. Our findings highlight the added value of XR to robot-assisted training and provide novel insights into the differences between AR and VR when integrated into a robotic training protocol. In addition, we developed a custom XR application that suited well for TruST intervention requirements. Our approach can be extended to other studies to develop novel XR-enhanced robotic training platforms.

Computer Vision and Augmented Reality for Human-Centered Fatigue Crack Inspection.

A significant percentage of bridges in the United States are serving beyond their 50-year design life, and many of them are in poor condition, making them vulnerable to fatigue cracks that can result in catastrophic failure. However, current fatigue crack inspection practice based on human vision is time-consuming, labor intensive, and prone to error. We present a novel human-centered bridge inspection methodology to enhance the efficiency and accuracy of fatigue crack detection by employing advanced technologies including computer vision and augmented reality (AR). In particular, a computer vision-based algorithm is developed to enable near-real-time fatigue crack detection by analyzing structural surface motion in a short video recorded by a moving camera of the AR headset. The approach monitors structural surfaces by tracking feature points and measuring variations in distances between feature point pairs to recognize the motion pattern associated with the crack opening and closing. Measuring distance changes between feature points, as opposed to their displacement changes before this improvement, eliminates the need of camera motion compensation and enables reliable and computationally efficient fatigue crack detection using the nonstationary AR headset. In addition, an AR environment is created and integrated with the computer vision algorithm. The crack detection results are transmitted to the AR headset worn by the bridge inspector, where they are converted into holograms and anchored on the bridge surface in the 3D real-world environment. The AR environment also provides virtual menus to support human-in-the-loop decision-making to determine optimal crack detection parameters. This human-centered approach with improved visualization and human-machine collaboration aids the inspector in making well-informed decisions in the field in a near-real-time fashion. The proposed crack detection method is comprehensively assessed using two laboratory test setups for both in-plane and out-of-plane fatigue cracks. Finally, using the integrated AR environment, a human-centered bridge inspection is conducted to demonstrate the efficacy and potential of the proposed methodology.

A prostate seed implantation robot system based on human-computer interactions: Augmented reality and voice control.

The technology of robot-assisted prostate seed implantation has developed rapidly. However, during the process, there are some problems to be solved, such as non-intuitive visualization effects and complicated robot control. To improve the intelligence and visualization of the operation process, a voice control technology of prostate seed implantation robot in augmented reality environment was proposed. Initially, the MRI image of the prostate was denoised and segmented. The three-dimensional model of prostate and its surrounding tissues was reconstructed by surface rendering technology. Combined with holographic application program, the augmented reality system of prostate seed implantation was built. An improved singular value decomposition three-dimensional registration algorithm based on iterative closest point was proposed, and the results of three-dimensional registration experiments verified that the algorithm could effectively improve the three-dimensional registration accuracy. A fusion algorithm based on spectral subtraction and BP neural network was proposed. The experimental results showed that the average delay of the fusion algorithm was 1.314 s, and the overall response time of the integrated system was 1.5 s. The fusion algorithm could effectively improve the reliability of the voice control system, and the integrated system could meet the responsiveness requirements of prostate seed implantation.

Augmented-reality-based surgical navigation for endoscope retrograde cholangiopancreatography: A phantom study.

BACKGROUND: Endoscope retrograde cholangiopancreatography is a standard surgical treatment for gallbladder and pancreatic diseases. However, surgeons is at high risk and require sufficient surgical experience and skills. METHODS: (1) The simultaneous localisation and mapping technique to reconstruct the surgical environment. (2) The preoperative 3D model is transformed into the intraoperative video environment to implement the multi-modal fusion. (3) A framework for virtual-to-real projection based on hand-eye alignment. For the purpose of projecting the 3D model onto the imaging plane of the camera, it uses position data from electromagnetic sensors. RESULTS: Our AR-assisted navigation system can accurately guide physicians, which means a distance of registration error to be restricted to under 5 mm and a projection error of 5.76 ± 2.13, and the intubation procedure is done at 30 frames per second. CONCLUSIONS: Coupled with clinical validation and user studies, both the quantitative and qualitative results indicate that our navigation system has the potential to be highly useful in clinical practice.

Comparison of subjective evaluations in virtual and real environments for soundscape researcha).

Emerging technologies of virtual reality (VR) and augmented reality (AR) are enhancing soundscape research, potentially producing new insights by enabling controlled conditions while preserving the context of a virtual gestalt within the soundscape concept. This study explored the ecological validity of virtual environments for subjective evaluations in soundscape research, focusing on the authenticity of virtual audio-visual environments for reproducibility. Different technologies for creating and reproducing virtual environments were compared, including field recording, simulated VR, AR, and audio-only presentation, in two audio-visual reproduction settings, a head-mounted display with head-tracked headphones and a VR lab with head-locked headphones. Via a series of soundwalk- and lab-based experiments, the results indicate that field recording technologies provided the most authentic audio-visual environments, followed by AR, simulated VR, and audio-only approaches. The authenticity level influenced subjective evaluations of virtual environments, e.g., arousal/eventfulness and pleasantness. The field recording and AR-based technologies closely matched the on-site soundwalk ratings in arousal, while the other approaches scored lower. All the approaches had significantly lower pleasantness ratings compared to on-site evaluations. The choice of audio-visual reproduction technology did not significantly impact the evaluations. Overall, the results suggest virtual environments with high authenticity can be useful for future soundscape research and design.

Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model.

This protocol helps assess the accuracy and workflow of an augmented reality (AR) hybrid navigation system using the Magic Leap head-mounted display (HMD) for minimally invasive pedicle screw placement. The cadaveric porcine specimens were placed on a surgical table and draped with sterile covers. The levels of interest were identified using fluoroscopy, and a dynamic reference frame was attached to the spinous process of a vertebra in the region of interest. Cone beam computerized tomography (CBCT) was performed, and a 3D rendering was automatically generated, which was used for the subsequent planning of the pedicle screw placements. Each surgeon was fitted with an HMD that was individually eye-calibrated and connected to the spinal navigation system. Navigated instruments, tracked by the navigation system and displayed in 2D and 3D in the HMD, were used for 33 pedicle cannulations, each with a diameter of 4.5 mm. Postprocedural CBCT scans were assessed by an independent reviewer to measure the technical (deviation from the planned path) and clinical (Gertzbein grade) accuracy of each cannulation. The navigation time for each cannulation was measured. The technical accuracy was 1.0 mm ± 0.5 mm at the entry point and 0.8 mm ± 0.1 mm at the target. The angular deviation was 1.5° ± 0.6°, and the mean insertion time per cannulation was 141 s ± 71 s. The clinical accuracy was 100% according to the Gertzbein grading scale (32 grade 0; 1 grade 1). When used for minimally invasive pedicle cannulations in a porcine model, submillimeter technical accuracy and 100% clinical accuracy could be achieved with this protocol.

Cutting-edge care: unleashing artificial intelligence's potential in gynecologic surgery.

PURPOSE OF REVIEW: Artificial intelligence (AI) is now integrated in our daily life. It has also been incorporated in medicine with algorithms to diagnose, recommend treatment options, and estimate prognosis. RECENT FINDINGS: AI in surgery differs from virtual AI used for clinical application. Physical AI in the form of computer vision and augmented reality is used to improve surgeon's skills, performance, and patient outcomes. SUMMARY: Several applications of AI and augmented reality are utilized in gynecologic surgery. AI's potential use can be found in all phases of surgery: preoperatively, intra-operatively, and postoperatively. Its current benefits are for improving accuracy, surgeon's precision, and reducing complications.