Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient\'s body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.

OBJECTIVE: Anatomical understanding is an important basis for medical teaching, especially in a surgical context. The interpretation of complex vascular structures via two-dimensional visualization can yet be difficult, particularly for students. The objective of this study was to investigate the feasibility of an MxR-assisted educational approach in vascular surgery undergraduate education, comparing an MxR-based teaching-intervention with CT-based material for learning and understanding the vascular morphology of the thoracic aorta.
METHODS: In a prospective randomized controlled trial learning success and diagnostic skills following an MxR- vs. a CT-based intervention was investigated in 120 thoracic aortic visualizations. Secondary outcomes were motivation, system-usability as well as workload/satisfaction. Motivational factors and training-experience were also assessed. Twelve students (7 females; mean age: 23 years) were randomized into two groups undergoing educational intervention with MxR or CT.
RESULTS: Evaluation of learning success showed a mean improvement of 1.17 points (max.score: 10; 95%CI: 0.36-1.97). The MxR-group has improved by a mean of 1.33 [95% CI: 0.16-2.51], against 1.0 points [95% CI: -0.71- 2.71] in the CT-group. Regarding diagnostic skills, both groups performed equally (CT-group: 58.25 ± 7.86 vs. MxR-group:58.5 ± 6.60; max. score 92.0). 11/12 participants were convinced that MxR facilitated learning of vascular morphologies. The usability of the MxR-system was rated positively, and the perceived workload was low.
CONCLUSIONS: MxR-systems can be a valuable addition to vascular surgery education. Further evaluation of the technology in larger teaching situations are required. Especially regarding the acquisition of practical skills, the use of MxR-systems offers interesting application possibilities in surgical education.

OBJECTIVE: The increasing popularity of three-dimensional (3D) virtual reconstructions of two-dimensional (2D) imaging in urology has led to significant technological advancements, resulting in the creation of highly accurate 3D virtual models (3DVMs) that faithfully replicate individual anatomical details. This technology enhances surgical reality, providing surgeons with hyper-accurate insights into instantaneous subjective surgical anatomy and improving preoperative surgical planning. In the uro-oncologic field, the utility of 3D virtual reconstruction has been demonstrated in nephron-sparing surgery, impacting surgical strategy and postoperative outcomes in prostate cancer (PCa). The aim of this study is to offer a thorough narrative review of the current state and application of 3D reconstructions and augmented reality (AR) in radical prostatectomy (RP).
METHODS: A non-systematic literature review was conducted using Medline, PubMed, the Cochrane Database, and Embase to gather information on clinical trials, randomized controlled trials, review articles, and prospective and retrospective studies related to 3DVMs and AR in RP. The search strategy followed the PICOS (Patients, Intervention, Comparison, Outcome, Study design) criteria and was performed in January 2024.
UNASSIGNED: The adoption of 3D visualization has become widespread, with applications ranging from preoperative planning to intraoperative consultations. The urological community\'s interest in intraoperative surgical navigation using cognitive, virtual, mixed, and AR during RP is evident in a substantial body of literature, including 16 noteworthy investigations. These studies highlight the varied experiences and benefits of incorporating 3D reconstructions and AR into RP, showcasing improvements in preoperative planning, intraoperative navigation, and real-time decision-making.
CONCLUSIONS: The integration of 3DVMs and AR technologies in urological oncology, particularly in the context of RP, has shown promising advancements. These technologies provide crucial support in preoperative planning, intraoperative navigation, and real-time decision-making, significantly improving the visualization of complex anatomical structures helping in the nerve sparing (NS) approach modulation and reducing positive surgical margin (PSM) rate. Despite positive outcomes, challenges such as small patient cohorts, lack of standardized methodologies, and concerns about costs and technology adoption persist.

Mixed reality technologies, such as virtual (VR) and augmented (AR) reality, present promising opportunities to advance education and professional training due to their adaptability to diverse contexts. Distortions in the perceived distance in such mediated conditions, however, are well documented and have imposed nontrivial challenges that complicate and limit transferring task performance in a virtual setting to the unmediated reality (UR). One potential source of the distance distortion is the vergence-accommodation conflict-the discrepancy between the depth specified by the eyes\' accommodative state and the angle at which the eyes converge to fixate on a target. The present study involved the use of a manual pointing task in UR, VR, and AR to quantify the magnitude of the potential depth distortion in each modality. Conceptualizing the effect of vergence-accommodation offset as a constant offset to the vergence angle, a model was developed based on the stereoscopic viewing geometry. Different versions of the model were used to fit and predict the behavioral data for all modalities. Results confirmed the validity of the conceptualization of vergence-accommodation as a device-specific vergence offset, which predicted up to 66% of the variance in the data. The fitted parameters indicate that, due to the vergence-accommodation conflict, participants\' vergence angle was driven outwards by approximately 0.2°, which disrupted the stereoscopic viewing geometry and produced distance distortion in VR and AR. The implications of this finding are discussed in the context of developing virtual environments that minimize the effect of depth distortion.

Augmented reality (AR) and virtual reality (VR), are upcoming technologies with considerable potential to revolutionizing healthcare education, enhancing patient safety, and improving healthcare quality particularly in the Indian context. This review is conducted to view the current scenario of Indian context considering the impact of COVID-19. The current systematic review study was done following PRISMA 2020 guidelines using the key terms \"Augmented Reality,\" \"Virtual Reality,\" \"Healthcare,\" and \"India.\" Only the PubMed database was selected based on its reputation and authenticity, which is the only limitation of this study and strength. Both qualitative and quantitative methods are used for synthesis of results. In Indian context, 12 (1.7%) and 36 (2.2%) articles related to AR and VR were found, respectively. Six abstracts could not be retrieved, and after screening abstracts, three were found not suitable in VR and eight were found duplicate. A total of 30 articles were considered for this review. 18 (50%) were original, 12 (33.3%) were review, and 6 (16.7%) were other articles. 03 (8.3%), 21 (58.3%), and 12 (33.3%) articles were related to AR, VR, and both AR and VR, respectively. Considering the single database search and six unretrievable abstract, AR, VR, mixed reality (MR), soft e-skin, and extended reality (XR) technologies have the potential to revolutionize healthcare education and training, reducing real-life errors and improving patient safety. Although the Indian healthcare sector only contributes 1.7-2.2% to PubMed publications related to AR and VR.. The review was not registered.

Sleep problems are highly prevalent in autism and negatively impact the physical and mental health of children and their caregivers. Sleep education programs are often recommended as a first line-treatment to help parents implement healthy sleeping habits and a bedtime routine at home; however, the accompanying paper-based toolkits used in the bedtime routines have limitations related to engagement and adherence. To address these gaps, we iteratively developed and tested the usability of an augmented reality (AR) bedtime routine application. Our single participant design study (n = 7 child/parent dyads) found 86% compliance with the program and suggested good-excellent usability of the app with a trend toward increased willingness and faster completion of children\'s bedtime routines. This work supports the feasibility of using technology-based tools in sleep education programs and informs future clinical studies examining the effectiveness of these approaches for mitigating sleep difficulties.

OBJECTIVE: Augmented reality navigation in liver surgery still faces technical challenges like insufficient registration accuracy. This study compared registration accuracy between local and external virtual 3D liver models (vir3DLivers) generated with different rendering techniques and the use of the left vs right main portal vein branch (LPV vs RPV) for landmark setting. The study should further examine how registration accuracy behaves with increasing distance from the ROI.
METHODS: Retrospective registration accuracy analysis of an optical intraoperative 3D navigation system, used in 13 liver tumor patients undergoing liver resection/thermal ablation.
RESULTS: 109 measurements in 13 patients were performed. Registration accuracy with local and external vir3DLivers was comparable (8.76 ± 0.9 mm vs 7.85 ± 0.9 mm; 95% CI = -0.73 to 2.55 mm; p = 0.272). Registrations via the LPV demonstrated significantly higher accuracy than via the RPV (6.2 ± 0.85 mm vs 10.41 ± 0.99 mm, 95% CI = 2.39 to 6.03 mm, p < 0.001). There was a statistically significant positive but weak correlation between the accuracy (dFeature) and the distance from the ROI (dROI) (r = 0.298; p = 0.002).
CONCLUSIONS: Despite basing on different rendering techniques both local and external vir3DLivers have comparable registration accuracy, while LPV-based registrations significantly outperform RPV-based ones in accuracy. Higher accuracy can be assumed within distances of up to a few centimeters around the ROI.

Historical data on monuments offers valuable insights into that period\'s past sculpture, architecture, and preferences. Realising the importance of historical data and the scarcity of data on historical places, this study presents a dataset collected from Panam City. Panam City, established in the late 1300s century, was the capital of the fifteenth-century Bengal ruler Isa Khan. The city was once an important trading and political centre and is now considered a world heritage site by the United Nations Educational Scientific and Cultural Organisation (UNESCO). Panam City is located in Sonargaon, Dhaka, Bangladesh. The aim of data collection is to capture past architectural design, materials used for the building, and the current state of the walls and structures of Panam City. This dataset can benefit researchers, architects, archaeologists, and cultural organisations. Historians and architects can gain insights into the wall\'s construction methods and materials, informing future restoration efforts. Historic datasets can create exciting AR/VR experiences by digitizing and 3D modelling historical artefacts and environments, integrating them into AR/VR platforms using game engines and development tools, and enhancing the user experience with interactive storytelling and educational content. Tourism boards and cultural heritage organisations can leverage this resource to develop engaging experiences that highlight the rich history and significance of Panama City. By making this data accessible, this study contributes to understanding and appreciating Panam City\'s historical significance while promoting innovative approaches to heritage preservation in the digital age. This dataset contains 2292 images of degraded wall classes such as Artistic, Corroded Brick, Corroded Plaster, Fungus, and Living Plant.

This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects\' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.
本研究探讨了一种基于增强现实（AR）环境和稳态视觉诱发电位（SSVEP）的脑机接口（BCI）系统，用于在真实场景下通过视觉注视完成对现实物品的选取任务。该系统结合了目标检测技术和AR技术，在真实物体上附加视觉增强效果，从而给予用户视觉刺激诱发相关脑电信号，再利用SSVEP技术解析脑电信号，完成对用户关注物体的识别。此外，本文采用了基于自适应动态时间窗的滤波器组典型相关分析算法实现对受试者脑电信号的快速解析。实验结果表明，该系统可以有效地识别解析SSVEP信号，对用户视觉目标的识别平均准确率达到了90.6%。该系统拓展了SSVEP信号在现实生活场景中的应用，在帮助行动不便和肢体功能受损患者进行物品选取任务上具备可行性和有效性。.

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a \"third hand\", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.
基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）在智能机器人领域的应用备受关注。传统基于SSVEP的BCI系统多采用同步触发方式，没有识别用户是处于控制态还是非控制态，导致系统缺乏自主控制能力。为此，本文提出了一种SSVEP异步状态识别方法，通过融合脑电信号（EEG）的多种时频域特征，结合线性判别分类器构建了异步状态识别模型，提高SSVEP异步状态识别准确率。进一步，针对残障人群在多任务场景下的控制需求，搭建了一种基于SSVEP-BCI异步协同控制的脑机融合系统，实现在复杂场景下可穿戴机械手与机械臂即“第三只手”的协同控制。实验结果表明，运用本文所提出的SSVEP异步控制算法和脑机融合系统，可以辅助用户完成多任务协同操作，在线控制实验中用户意图识别的平均准确率为93.0%，为SSVEP异步脑机接口系统的实际应用提供了理论和实践依据。.