Application Effect Study of a New Training Model Combining Extended Reality Technology and 3D Printing Models in Percutaneous Balloon Compression-Accurate Puncture of the Foramen Ovale

Application Effect Study of a New Training Model Combining Extended Reality Technology and 3D Printing Models in Percutaneous Balloon Compression-Accurate Puncture of the Foramen Ovale | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application Effect Study of a New Training Model Combining Extended Reality Technology and 3D Printing Models in Percutaneous Balloon Compression-Accurate Puncture of the Foramen Ovale Zhengyuan Xie, Zongyuan Jiang, Shao'ai Chen, Xiaohui Li, Jiyong Gu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7556990/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective Owing to the complex structure of the skull base, accurate puncture of the foramen ovale in percutaneous balloon compression (PBC) is critical for surgical success. In traditional training models, trainees often struggle to meet the needs of precise training because of the lack of real operational simulation scenarios. This study aims to explore the application feasibility and effectiveness of a new training model that combines extended reality (XR) technology and 3D printing models in precise training for PBC foramen ovale puncture. Design Thirty participating physicians were randomly divided into a traditional group (n = 15) and a study group (n = 15). The traditional group received CT image interpretation by attending physicians with PBC experience, anatomical structure explanation of the foramen ovale using skull specimens, teaching of puncture point localization, path planning, and operation key points combined with Hartel's anterior localization method, followed by puncture training on a 1:1 ratio 3D printed model. The study group, on the basis of theoretical explanations, used XR technology for preoperative anatomical structure visualization simulation and puncture path planning, followed by hands-on practice with 3D printed models. Indicators such as the first successful puncture time and number of punctures were recorded, and training effects were comprehensively evaluated through operation scores and questionnaires. Participants The study included 30 resident physicians working and training in the Department of Neurosurgery at Jiangmen Central Hospital. Results The first successful puncture time (1.90 ± 0.15 min) and number of punctures (1.33 ± 0.13) in the study group were significantly greater than those in the traditional group (6.33 ± 0.61 min, 2.27 ± 0.25 times, P < 0.001). All physicians in the study group successfully completed foramen ovale localization and puncture operations and accurately identified the anatomical structures of the model ( P < 0.05). The localization accuracy score (5 points, IQR 5–5) and comprehensive operation score (5 points, IQR 5–5) of the study group were significantly higher than those of the traditional group (3 points, IQR 2–4; 4 points, IQR 1–4, P 0.05). After training, the clinical confidence of the physicians in the study group significantly improved ( P < 0.05). One hundred percent of the physicians in the study group recognized that this pattern promoted learning interest and self-confidence. Conclusion A training pattern that combines XR technology and 3D printing models can strengthen trainees' spatial understanding of anatomical structures through virtual-real fusion interactive operations, shorten the learning curve, increase trainee precision and psychological confidence in clinical practice, and provide an innovative solution for standardized training involving minimally invasive interventional techniques. Extended reality technology 3D printing model Percutaneous balloon compression Foramen ovale puncture Medical education training Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction In the past decade, PBC has become the mainstream minimally invasive procedure for treating trigeminal neuralgia in China [ 1 – 3 ]. However, traditional foramen ovale puncture methods rely heavily on the surgeon's experience and proficiency. Most domestic units currently perform the operation under the monitoring of a C-arm X-ray machine or digital subtraction angiography (DSA), which poses two major challenges for beginners: ① Anatomical variations of the skull base (approximately 2–4% of patients experience puncture failure due to anatomical abnormalities) [ 4 ]; ② two-dimensional images make it difficult to intuitively present three-dimensional spatial relationships, and repeated punctures may cause serious complications [ 5 – 6 ]. The current domestic training model is dominated by "theoretical lectures + anatomical atlases + surgical observation," lacking independent operation training, leading to a long learning curve. In recent years, with the widespread application of computer image processing technology in the medical field, technologies such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and 3D printing have gradually been integrated into modern medical education and training, greatly promoting the development of surgical planning and simulation [ 7 – 11 ]. XR technology, which more widely integrates VR, AR, and MR technologies, has currently demonstrated significant clinical value in the medical field. Using XR technology in clinical simulation training can involve superimposing 3D virtual anatomical models onto the surface of human models to identify internal anatomical structures and landmarks, interact, provide feedback, and adjust in real time with virtual models to meet different teaching needs. This real-time visualization operation training can increase trainees' operational confidence and accumulate experience, laying a solid foundation for future practical operations [ 12 ] without compromising patient safety [ 13 ]. For example, VR technology platforms are used for teaching human anatomy [ 14 – 15 ] and training surgeons in complex surgical procedures [ 16 – 19 ]. AR and MR provide methods for visualizing intraoperative procedures and imaging via devices such as Google Glass (Google, Inc.) or Microsoft HoloLens (Microsoft, Inc.), which may improve surgical safety and success rates [ 20 – 22 ]. In addition, they are applied in preoperative surgical planning [ 23 – 24 ], intraoperative navigation [ 25 – 27 ], improving patient care [ 28 – 29 ], patient education [ 30 – 31 ], and doctor‒patient communication [ 32 ]. Considering the complexity and risks of neurosurgical operations, which require a high level of surgical skills and familiarity with anatomy, the use of XR technology has unique potential in the field of neurosurgery [ 33 ]. 3D printing technology provides a safe and repeatable physical operation platform for personalized surgical training through 1:1 highly simulated cranial models [ 34 – 35 ]. Operations such as skin surface localization, puncture point marking, and puncture can be performed on the printed model, which can more realistically reflect the actual surgical situation and can be repeatedly performed without risk. Given the absence of similar training models in China, this study aims to explore the application value of XR technology combined with 3D printing models in precise training for PBC foramen ovale puncture. We hope that this new training pattern can improve the accuracy and training efficiency of foramen ovale puncture through closed-loop training involving "virtual anatomical cognition - physical operation verification." We also hypothesize that this new training pattern can enable participants to better master this operation and can be applied to other similar types of surgical training. Materials and methods Research Ethics This study was approved by the Ethics Committee of Jiangmen Central Hospital (approval no. [2021] 17) according to the Declaration of Helsinki. And all the participants provided written informed consent. Realization of XR Technology by Study Group CT data from a 21-year-old healthy young male were collected. After providing consent, the data were imported into the free open-source software 3D Slicer (version 5.1.0, Surgical Planning Laboratory, Harvard Medical School) in Digital Imaging and Communications in Medicine (DICOM) format. First, the data were anonymized to protect patient privacy. The relevant anatomical structures were then three-dimensionally reconstructed and observed, and the puncture path was simulated with reference to the classic Hartel anterior localization method [ 36 ] (Figs. 1 A, B): 2–3 cm lateral to the corner of the mouth on the affected side. The other two reference points are located at the intersection of the line from the puncture point to the ipsilateral pupil and the inferior orbital margin and 3 cm anterior to the external auditory meatus at the inferior margin of the zygomatic arch on the puncture point side. The reconstructed puncture path model was displayed in perspective mode according to the actual surgical operation in 3D slicer software (Fig. 1 C) to achieve preoperative VR technology. Finally, the preoperative planned puncture path model was exported in ".Obj" format for the next MR technology step. The head-mounted MR device HoloLens 2 (hereinafter referred to as HL-2) was subsequently selected as the puncture guidance device. The research team developed a new model loading method for HL-2 and software based on the device's depth sensor and active infrared tracking and deployed the user interface on a personal laptop via the Python high-level programming language to ensure that surgical operators can intuitively interact with the system (Fig. 2 A). After the successful development of this software, it can realize mixed reality neuronavigation (MRN). The specific operations are as follows: turn on the HoloLens control system on the laptop, start the xmuIR application on the HL-2 device, and after successful startup, the control panel can be seen on the HL-2 display (Fig. 2 B, red box). Then, the username and password are entered into the user interface of the laptop. After the device is successfully connected, the "Send Address" button is clicked next to it to start downloading the model on HL-2. After the download is complete, the downloaded model can be seen through the display on HL-2 (Fig. 2 B, yellow box). Then, the head was moved to ensure that the HL-2 device was accurately aligned with the 3D-printed positioning probe. The infrared camera automatically identifies the optical passive reflective spheres on the probe and immediately presents a holographic image of the virtual probe matching the probe on the device display (Fig. 2 C). Using the tip of the probe, according to the order of the reconstructed markers, voice "Add" or click the "Add Point" button on the computer user interface at the corresponding anatomical positions to confirm the addition of virtual coordinate points one by one. After completing the addition of coordinate points, "Register" or "Register," and the system automatically starts the point-based registration algorithm for registration. Once the registration is successful, the reconstructed target model automatically aligns with the registered 3D-printed model while the bilateral foramen ovale and puncture path are displayed (Fig. 2 D). Generation of 3D Printed Models The printing data of the model were the same as the above-provided data, and 3D Slicer software (version 5.1.0) was also used for three-dimensional reconstruction of anatomical structures such as the scalp and skull (Fig. 3 A, B). The file was then saved in 3D printing format (.stl) and printed via a 3D printing machine (Zhuhai Seine Printing Technology Co., Ltd., model: J401pro) at the Digital Medicine and Additive Manufacturing Engineering Technology Research Center of Jiangmen Central Hospital. The skull part was made of resin material, and the skullcap and skull base bone could be separated to facilitate observation of the internal anatomical structures of the skull and the actual puncture process; the skin part used flexible resin to support repeated puncture operations (Fig. 3 C, D). Participants and evaluation Thirty resident physicians in the Department of Neurosurgery at Jiangmen Central Hospital who had not received similar training between March 2022 and March 2025 were included and randomly divided into two groups (15 people in each group). All participants first received 20 minutes of theoretical teaching, which included identification of the foramen ovale on CT images and skull samples, positioning and line drawing, puncture direction, and operational precautions (Figs. 4 A-D). After the explanation, the traditional group directly performed puncture operations on the 3D printed model, whereas the study group first performed three-dimensional reconstruction of the model on the computer according to Hartel's anterior localization method, observed the relevant anatomical structures, and formulated a puncture path, which took approximately 15–20 minutes. HL-2 was subsequently used to perform foramen ovale puncture operations on the 3D-printed model (Fig. 4 E-F). According to the puncture operation experience of the instructing physicians, relevant data on the operations of the two groups were collected. The contents included identification of the foramen ovale on the model, description and operation of puncture positioning, the time taken for the first successful puncture in the two groups, the number of punctures, and the number of puncture failures (puncture time > 10 minutes or ≥ 3 punctures). Moreover, the confidence of the participants in the two groups in the clinical operations before and after training was also investigated. Finally, a comprehensive scoring method was used to conduct an overall operation evaluation of the participants. Anonymous questionnaires were used to collect feedback from the participants. For puncture positioning and comprehensive scoring, a 5-point scoring standard was adopted, from a minimum of 1 point to a maximum of 5 points; the questionnaire used a 5-point scale from "very dissatisfied" to "very satisfied." The contents of the puncture positioning score include the following: ① Description of Hartel's anterior localization method before the operation: 1 point ② Placement of the model position: 1 point ③ Depiction of the positioning line on the model: 1 point ④ Puncture depth: 1 point ⑤ Whether the puncture was successful: 1 point The comprehensive operation score was evaluated according to the following items, with a minimum of 1 point and a maximum of 5 points: ① Theoretical knowledge ② Model positioning ③ Puncture operation process ④ Accuracy of the puncture position ⑤ Smoothness of the entire operation process Data Statistical Analysis Before performing parametric statistical analysis, the Kolmogorov‒Smirnov test (KS test) was used for normality analysis of the data distribution. Baseline demographic and clinical characteristics are summarized as the mean (mean) ± standard error (SE) for normally distributed continuous variables, the median (interquartile range, IQR) for nonnormally distributed continuous variables, and the frequency (percentage) for categorical variables. The Mann‒Whitney U test was used to evaluate nonnormally distributed variables. In addition, the comparisons are represented by bar charts, scatter plots, and violin plots of the median and quartile values. The chi-square test or Fisher's exact test was used to compare differences in categorical data. Statistical analysis was performed via the Statistical Package for the Social Sciences (SPSS®, version 25.0; Chicago, Illinois, USA). P < 0.05 was considered statistically significant. Results Characteristics of the participants As shown in Table 1 , the baseline characteristics of the two groups were extremely similar, so randomization of the groups was successfully achieved. The traditional group had an average age of 26.53 ± 1.30 years (13 males and 2 females), and the study group had an average age of 26.73 ± 1.62 years (15 males). There was no significant difference in age or sex (P > 0.05). Table 1 Basic characteristics of the two Groups of trained physicians Traditional group Study group P value Number of trainees(n) 15 15 Gender (male/female in %) 13/2 15/0 0.483* Age(mean ± standard deviation) 26.53 ± 1.30 26.73 ± 1.62 0.713# * Chi-square test. # Independent sample t test. Puncture Operation Results The time required for the foramen ovale in the study group to be successfully punctured for the first time (1.90 ± 0.15 minutes) and the number of punctures (1.33 ± 0.13) were significantly greater than those in the traditional group (6.33 ± 0.61 minutes and 2.27 ± 0.25 times, respectively) (P < 0.001, Table 2 , Figs. 5 A-B). Five physicians (33%) in the traditional group failed to perform the puncture successfully, and six physicians (40%) could not accurately identify the foramen ovale on the printed model. All the physicians in the study group successfully completed the puncture and could accurately identify the foramen ovale on the model (P < 0.05, Table 2 ). In addition, in terms of positioning accuracy, the scoring results revealed that the traditional group scored between 3 (IQR, 2–4), and the comprehensive operation score showed a similar trend: the traditional group scored an average of approximately 4 (IQR, 1–4), whereas the study group scored 5 (IQR, 5–5) for both items (P < 0.001, Table 2 , Figs. 6 A - B). However, in terms of practical clinical operation confidence, the study group significantly improved after training compared with the traditional group (12/15 vs 5/15), and this difference was statistically significant (P < 0.05, Table 2 ). Table 2 Two sets of operation results Traditional group Study group P value First successful puncture time (min) 6.33 ± 0.61 1.90 ± 0.15 <0.001 # Number of punctures (n) 2.27 ± 0.25 1.33 ± 0.13 <0.001 # Number of puncture failures (n) 5(15) 0(15) 0.042 * Identify the number of foramen ovale (n) 9(15) 15(15) 0.017 * Puncture localization 3(2, 4) 5(5, 5) <0.001 △ Operational comprehensive score 4(1, 4) 5(5, 5) <0.001 △ Confidence in practical operation before training 3(15) 2(15) 1 * Confidence in practical operation after training 5(15) 12(15) 0.017 * * Chi-square test. # Independent sample t test. △Mann-Whitney U test. Subjective Questionnaire Results As shown in Figs. 7 A - B, 73.3% of the trained physicians in the traditional group "strongly agreed" or "agreed" with this training pattern, and 53.3% of the physicians believed that, compared with pure theoretical lectures, this teaching method was better and of great help to them. In the study group, both items reached 100%, 86.7% of which were "strongly agreed." A total of 86.7% of the training physicians in both groups "strongly agreed" or "agreed" that using 3D printed models could better master anatomical structures and improve practical operation capabilities. A total of 73.3% of the physicians in the study group "strongly agreed" that the training pattern of this group could increase their confidence in operations and make them more confident in future work, whereas 26.7% of the physicians in the traditional group "disagreed" that the training pattern would improve their confidence. In addition, 93.3% of the doctors in the study group said they hoped that other subjects would also adopt this teaching method, whereas only 53.3% of those in the traditional group did. Discussion PBC has gained widespread clinical application globally because of its simple operation and high degree of safety [ 37 ]. However, the current training system still relies mainly on theoretical teaching by senior physicians combined with skull models and lacks practical operation links in real scenarios. In clinical practice, physicians typically rely on fluoroscopy or intraoperative CT for positioning and operation monitoring, which significantly increases the risk of radiation exposure for both doctors and patients. On this basis, this study aims to develop an innovative training pattern to help beginners achieve precise foramen ovale punctures, thereby improving teaching efficiency and quality. The iterative increase in computing power has driven significant progress in VR, AR, and MR technologies. As a general term for the abovementioned technologies, XR technology refers to creating a digital environment that combines reality and virtuality through modern high-tech means with a computer as the core, as well as new human‒computer interaction methods, providing an immersive experience of seamless conversion between the virtual and real worlds to users. This technical system is currently rapidly popularizing in the field of neurosurgery and continues to develop. In PBC-related operations, identification of the foramen ovale via preoperative imaging has become the core step in surgical planning. Although thin-slice CT scans provide clearer observations of skull base bones, it is still difficult for some junior physicians to understand the complex three-dimensional skull base structure from two-dimensional images [ 38 ]. VR technology can reconstruct two-dimensional image data into three-dimensional models, allowing trainees to observe the foramen ovale and surrounding anatomical structures intuitively, measure relevant parameters, and assess puncture difficulty. Although it only displays three-dimensional images on a computer instead of superimposing the reconstructed content onto the real environment, this teaching pattern can arouse trainees' interest in learning and visualize abstract content. Moreover, VR-simulated puncture can improve the success rate, creating familiarity during actual operations to enhance educational effects. As early as 2014, Shakur [ 39 ] et al. used a VR simulator developed on the ImmersiveTouch platform for simulated puncture training of the foramen ovale. Junior physicians using VR technology to simulate surgery before the operation help quickly improve surgical skills and shorten the learning curve [ 40 ]. Studies have shown that learning anatomy and surgical paths in a virtual environment may help retain and recall the learning process [ 41 – 44 ]. The data of this study further verify the teaching value of this technology: physicians in the study group who received VR training achieved a 100% accuracy rate in identifying foramen ovale in 3D printed models, whereas 40% of physicians in the traditional group failed to complete this task. In addition, 86.7% of the physicians in the study group "strongly agreed" with the VR teaching model, indicating that VR training can better promote beginners' understanding and mastery of the anatomical structures and related knowledge around the surgery. The emergence and application of AR and MR technologies have opened new avenues for neurosurgical teaching and personalized, precise surgery. Studies have reported that AR/MR surgical simulators are more suitable for surgical skill training needs than traditional VR simulators are because of their interactive characteristics of virtual-real fusion. Currently, MR surgical simulators, with their advantages of risk-free and low-cost training, are gradually becoming an important part of junior doctor training. By constructing immersive operation scenarios, trainees can experience complex surgical procedures and anatomical structures in a zero-risk environment, which helps junior doctors grow faster and deepen their understanding of complex surgeries. Hooten et al. [ 45 ] developed a novel physical‒virtual hybrid simulator for simulating ventricular puncture. Peng et al. [ 46 ] successfully constructed a training system for basal ganglia hematoma puncture and anterior ventricular horn puncture by combining 3D printed models with head-mounted mixed reality devices. Inspired by this, this study attempts to couple XR technology with 3D-printed models to construct a simulated surgical scenario that integrates anatomical structures such as the scalp and skull. This model not only addresses the inherent defects of difficult acquisition and cumbersome preservation of cadaver head specimens but also addresses the technical bottleneck of the difficult quantification of puncture accuracy in traditional cadaver head operations through visualization of puncture paths and real-time effect evaluation. 3D-printed models have been proven beneficial for students in surgical education [ 47 ]. Therefore, although the current cost of 3D printing is still high, it remains a worthwhile method (this project is funded by the Key Project of Basic and Applied Basic Research in Jiangmen City). The questionnaire data revealed that 86.7% of the physicians in both the traditional and study groups approved the 3D-printed model, indicating that it helps them better master anatomical structures and improves practical operation capabilities. The virtual-hybrid reality surgical training platform constructed by the collaboration of XR technology and 3D-printed models compensates for the deficiency in simulation realism when VR technology or 3D-printed models alone are used. This platform integrates the virtual and real worlds, allowing beginners to complete operations on 1:1 highly simulated anatomical models and obtain visual feedback highly similar to that of human surgery without entering the operating room. Rau et al. [ 48 ] used AR technology to guide foramen ovale puncture in a specially designed model and concluded that AR technology greatly improved the accuracy of foramen ovale intubation. The physicians in the study group reported that the most profound feeling during training was that this new immersive teaching method had a strong visual impact, giving them a sense of perspective operation and enhancing participants' confidence in unfamiliar surgical operations [ 49 ]. Studies have shown that, compared with traditional clinical teaching methods, XR technology has significant advantages in clinical skill training [ 50 ]. Compared with traditional methods, which often lack a three-dimensional sense and are relatively monotonous, the XR training platform allows trainees to experience operation processes in a highly realistic environment and intuitively observe relevant anatomical structures from multiple angles. Trainees can also use holographic images to select the best surgical path for repeated practice, thereby improving their understanding of space and direction, mastering operation skills more efficiently, shortening the learning curve, and enabling them to operate more confidently and accurately in future work [ 51 ]. The data show that the study group significantly outperformed the traditional group in terms of first-puncture success time, number of punctures, positioning accuracy, and final scores. After this training method, physicians showed significantly increased confidence in actual surgeries, with 73.3% of training physicians "strongly agreeing" that this training method enhanced their operational confidence. In contrast, only 33% of the physicians in the traditional training group "agreed" with their training method, considering that it was insufficiently effective. The questionnaire results revealed that 100% of the physicians in the study group believed that this teaching method enhanced their operational confidence, made them more confident in future work, and looked forward to applying this teaching model to other subjects. Limitations Although this study aims to improve the success rate and operational confidence of beginners in foramen ovale puncture during PBC surgery through a new training model, there is still room for optimization in terms of technical simulation accuracy, sample universality, and software-hardware systems: 1. The current training system cannot fully replicate key clinical details of PBC surgery, such as whether the puncture accurately enters Meckel's cave and whether a typical "pear-shaped expansion" can be formed after balloon implantation. Scenarios requiring biomechanical feedback and anatomical deformation simulation urgently need to be realized by higher-precision physical simulation devices. 2. The MR technology software used in this study was independently developed by the research team. Similar to mainstream XR technology projects in industry at this stage, scientific research cooperation involves medical-engineering integration. The developed software needs to undergo standardized evaluation and further verification through clinical transformation. 3. The sample of this study included only resident physicians from a single institution, without including clinical physicians at the attending level and above. The differences in the adaptability of physicians with different professional backgrounds and seniority levels to the training model have not been explored. In addition, the extrapolation of single-institution research results is limited, and it is necessary to expand the sample size and carry out multicenter research to comprehensively verify the clinical applicability of this training model. 4. The printing of high-precision anatomical models takes a long time and incurs high material costs, restricting the large-scale application of the training system; the current MR system has technical problems such as registration accuracy fluctuations and image drift, which need to improve interaction stability through hardware iteration and algorithm optimization. Future research needs to continue exploring in the directions of deepening simulation technology, multicenter clinical verification, and software-hardware upgrading to promote the clinical transformation and standardized application of this training pattern. Conclusion This study confirms the significant feasibility of applying the coupling of XR technology and 3D printed models to the PBC training system. This pattern enables trainees to deeply familiarize themselves with anatomical structures in an immersive environment by constructing virtual-real integrated operation scenarios and stimulating learning initiatives through interactive training. Repeated simulated operations not only accelerate the accumulation of clinical experience and strengthen operational confidence but also achieve a breakthrough in traditional models in terms of teaching efficiency, significantly improving the mastery efficiency of complex procedures through visual anatomical reconstruction and real-time feedback mechanisms. With the deepening of interdisciplinary technology integration, the cross-innovation of artificial intelligence, medical imaging, and educational science is reshaping the paradigm of medical training. In the current technical system, the iterative upgrade of device hardware (such as higher-resolution MR headsets and biomechanical simulation models) and the optimization of software algorithms (such as AI-assisted path planning) further enhance the realism and intelligence level of training scenarios. This multitechnology collaborative training model is not only suitable for PBC surgery but also expected to become a universal solution in the field of surgical education. Future research will focus on two major directions: ① expanding the applicability of this training model to other neurosurgical procedures (such as brain tumor resection and spinal puncture) and disciplines such as general surgery and orthopedics; and ② promoting its deep integration with the formal standardized training system for resident physicians, verifying teaching effects through multicenter clinical research, and gradually constructing a standardized new medical education ecosystem. This training paradigm, which is based on technological innovation, is opening a broad path for the digital transformation of medical education because of its unique interactivity and repeatability. Declarations Funding This study was funded by Jiangmen Science and Technology Commissioner Research Project​ (grant number: 2024760000310010369) and Jiangmen Central Hospital Education Reform Research Project (grant number: JG202401). Author Contribution Yilong peng，Zhengyuan Xie and Zongyuan Jiang have made substantial contributions to the design of the work , writing the main manuscript text and analysis of data; Shaoai Chen, Xiaohui Li, Jiyong Gu, Jinhong Li, Jiajia Yu and Ronglve Wei have made substantial contributions to the acquisition of data; Zhongjie Shi and Yilong Peng have made substantial contributions to the creation of the new software used in the work; All authors reviewed the manuscript. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 Nov, 2025 Reviews received at journal 06 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers invited by journal 30 Oct, 2025 Editor assigned by journal 29 Oct, 2025 Editor invited by journal 07 Oct, 2025 Submission checks completed at journal 05 Oct, 2025 First submitted to journal 05 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":361348,"visible":true,"origin":"","legend":"\u003cp\u003eVR technology is utilized for 3D reconstruction and simulated puncture. (A) The traditional Hartel anterior approach involves designing the puncture path at a distance of 2-3cm away from the corner of the mouth, with a reconstructed puncture needle indicated by a black arrow and a distance of 71.92 mm from skin to foramen ovale represented by a red box. (B) Thin-slice CT imaging allows visualization of anatomical structures such as the skull base and foramen ovale, enabling measurement of their size. (C) Display the reconstructed puncture path model in perspective mode according to the actual surgical operation. (blue).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7556990/v1/3a2bddeb634bcadc74d0c3e7.png"},{"id":95567016,"identity":"9c7d42d5-a7db-4e44-8e64-c6cbd9cd4c9d","added_by":"auto","created_at":"2025-11-10 16:21:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":782427,"visible":true,"origin":"","legend":"\u003cp\u003eImplementation of MR technology. (A) User interface deployed on a personal laptop computer. (B) After the successfully launched developed software, the control panel can be seen on the HL-2 device display screen (the part in the red box in Figure 2B). (C) The infrared camera on the HL-2 device automatically identifies the optical passive reflective balls on the 3D-printed probe, and immediately presents a virtual probe holographic image matching the probe on the device's display screen. (D) After successful registration, the reconstructed target model will be automatically aligned with the registered 3D-printed model, and at the same time, the bilateral foramen ovale and puncture paths will be displayed.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7556990/v1/66448b637fdcc7c76837fc68.png"},{"id":95655751,"identity":"72b01d98-226e-477c-ab05-fd2e364540df","added_by":"auto","created_at":"2025-11-11 16:16:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":284930,"visible":true,"origin":"","legend":"\u003cp\u003eProduction and finished product of 3D printing model. (A), (B) 3D slicer reconstructed model. (C), (D) The printed model allows the skull and skull base bones to be separated. The skin part is made of flexible resin, supporting repeated puncture operations.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7556990/v1/5daa2fecac82c1d53549c37f.png"},{"id":95655851,"identity":"154d115a-f5f0-4a9c-8583-80f2f77f884a","added_by":"auto","created_at":"2025-11-11 16:17:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":670547,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical teaching content and puncture operations guided by MR technology. (A) CT image shows the anatomical structure of the skull base. (B) The physician uses MR guidance to perform the puncture on the 3D printed model and records the time. (C), (D) Perform positioning, line drawing and puncture operations on the 3D-printed model. (E), (F) Puncture operation of the foramen ovale on the 3D-printed model under the guidance of the HL-2 device.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7556990/v1/09f1bf39b957ee4ebb9dfce4.png"},{"id":95567015,"identity":"26e54f91-fa4f-4c99-9ac0-8f8a1085160e","added_by":"auto","created_at":"2025-11-10 16:21:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41679,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of two sets of operations on the 3D-printed model. Column chart for comparing the time to first puncture in the model using the t-test. (A) Time to first puncture in the traditional group and the study group. (B) The number of punctures in the traditional group and the study group.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7556990/v1/d8de94bc76aa11fab176a565.png"},{"id":95567018,"identity":"24fcb07e-a0d7-4a49-9557-f4dc10abd01f","added_by":"auto","created_at":"2025-11-10 16:21:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":46731,"visible":true,"origin":"","legend":"\u003cp\u003ePuncture positioning score and comprehensive score after operation in traditional group and study group. (A) Scatter plot for comparing the results of different puncture localization using Mann–Whitney U test between the two groups. (B) Violin plot for comparing comprehensive score after operation using Mann–Whitney U test between the two groups.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7556990/v1/1a44020097265a4a97508e1b.png"},{"id":95656054,"identity":"9692ce25-285a-4df5-a798-a87b6d04b564","added_by":"auto","created_at":"2025-11-11 16:17:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":92949,"visible":true,"origin":"","legend":"\u003cp\u003eSubjective feedback of two groups of trained physicians on Likert scale. (A) Subjective feedback of trained physicians in the traditional group. (B) Subjective feedback of trained physicians in the study group.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7556990/v1/90e100a1e933e9b696f684b2.png"},{"id":95660194,"identity":"fd69e82f-6be2-47e1-b314-00ef97a9b871","added_by":"auto","created_at":"2025-11-11 16:31:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3374122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7556990/v1/0808f77b-11b4-4c6f-9576-9da57fc7d0b5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application Effect Study of a New Training Model Combining Extended Reality Technology and 3D Printing Models in Percutaneous Balloon Compression-Accurate Puncture of the Foramen Ovale","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the past decade, PBC has become the mainstream minimally invasive procedure for treating trigeminal neuralgia in China [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, traditional foramen ovale puncture methods rely heavily on the surgeon's experience and proficiency. Most domestic units currently perform the operation under the monitoring of a C-arm X-ray machine or digital subtraction angiography (DSA), which poses two major challenges for beginners: ① Anatomical variations of the skull base (approximately 2\u0026ndash;4% of patients experience puncture failure due to anatomical abnormalities) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; ② two-dimensional images make it difficult to intuitively present three-dimensional spatial relationships, and repeated punctures may cause serious complications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The current domestic training model is dominated by \"theoretical lectures\u0026thinsp;+\u0026thinsp;anatomical atlases\u0026thinsp;+\u0026thinsp;surgical observation,\" lacking independent operation training, leading to a long learning curve.\u003c/p\u003e\u003cp\u003eIn recent years, with the widespread application of computer image processing technology in the medical field, technologies such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and 3D printing have gradually been integrated into modern medical education and training, greatly promoting the development of surgical planning and simulation [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. XR technology, which more widely integrates VR, AR, and MR technologies, has currently demonstrated significant clinical value in the medical field. Using XR technology in clinical simulation training can involve superimposing 3D virtual anatomical models onto the surface of human models to identify internal anatomical structures and landmarks, interact, provide feedback, and adjust in real time with virtual models to meet different teaching needs. This real-time visualization operation training can increase trainees' operational confidence and accumulate experience, laying a solid foundation for future practical operations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] without compromising patient safety [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For example, VR technology platforms are used for teaching human anatomy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and training surgeons in complex surgical procedures [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. AR and MR provide methods for visualizing intraoperative procedures and imaging via devices such as Google Glass (Google, Inc.) or Microsoft HoloLens (Microsoft, Inc.), which may improve surgical safety and success rates [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, they are applied in preoperative surgical planning [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], intraoperative navigation [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], improving patient care [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], patient education [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and doctor‒patient communication [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Considering the complexity and risks of neurosurgical operations, which require a high level of surgical skills and familiarity with anatomy, the use of XR technology has unique potential in the field of neurosurgery [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e3D printing technology provides a safe and repeatable physical operation platform for personalized surgical training through 1:1 highly simulated cranial models [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Operations such as skin surface localization, puncture point marking, and puncture can be performed on the printed model, which can more realistically reflect the actual surgical situation and can be repeatedly performed without risk.\u003c/p\u003e\u003cp\u003eGiven the absence of similar training models in China, this study aims to explore the application value of XR technology combined with 3D printing models in precise training for PBC foramen ovale puncture. We hope that this new training pattern can improve the accuracy and training efficiency of foramen ovale puncture through closed-loop training involving \"virtual anatomical cognition - physical operation verification.\" We also hypothesize that this new training pattern can enable participants to better master this operation and can be applied to other similar types of surgical training.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch Ethics\u003c/h2\u003e\u003cp\u003eThis study was approved by the Ethics Committee of Jiangmen Central Hospital (approval no. [2021] 17) according to the Declaration of Helsinki. And all the participants provided written informed consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRealization of XR Technology by Study Group\u003c/h3\u003e\n\u003cp\u003eCT data from a 21-year-old healthy young male were collected. After providing consent, the data were imported into the free open-source software 3D Slicer (version 5.1.0, Surgical Planning Laboratory, Harvard Medical School) in Digital Imaging and Communications in Medicine (DICOM) format. First, the data were anonymized to protect patient privacy. The relevant anatomical structures were then three-dimensionally reconstructed and observed, and the puncture path was simulated with reference to the classic Hartel anterior localization method [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B): 2\u0026ndash;3 cm lateral to the corner of the mouth on the affected side. The other two reference points are located at the intersection of the line from the puncture point to the ipsilateral pupil and the inferior orbital margin and 3 cm anterior to the external auditory meatus at the inferior margin of the zygomatic arch on the puncture point side. The reconstructed puncture path model was displayed in perspective mode according to the actual surgical operation in 3D slicer software (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) to achieve preoperative VR technology. Finally, the preoperative planned puncture path model was exported in \".Obj\" format for the next MR technology step.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe head-mounted MR device HoloLens 2 (hereinafter referred to as HL-2) was subsequently selected as the puncture guidance device. The research team developed a new model loading method for HL-2 and software based on the device's depth sensor and active infrared tracking and deployed the user interface on a personal laptop via the Python high-level programming language to ensure that surgical operators can intuitively interact with the system (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). After the successful development of this software, it can realize mixed reality neuronavigation (MRN). The specific operations are as follows: turn on the HoloLens control system on the laptop, start the xmuIR application on the HL-2 device, and after successful startup, the control panel can be seen on the HL-2 display (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, red box). Then, the username and password are entered into the user interface of the laptop. After the device is successfully connected, the \"Send Address\" button is clicked next to it to start downloading the model on HL-2. After the download is complete, the downloaded model can be seen through the display on HL-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, yellow box). Then, the head was moved to ensure that the HL-2 device was accurately aligned with the 3D-printed positioning probe. The infrared camera automatically identifies the optical passive reflective spheres on the probe and immediately presents a holographic image of the virtual probe matching the probe on the device display (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Using the tip of the probe, according to the order of the reconstructed markers, voice \"Add\" or click the \"Add Point\" button on the computer user interface at the corresponding anatomical positions to confirm the addition of virtual coordinate points one by one. After completing the addition of coordinate points, \"Register\" or \"Register,\" and the system automatically starts the point-based registration algorithm for registration. Once the registration is successful, the reconstructed target model automatically aligns with the registered 3D-printed model while the bilateral foramen ovale and puncture path are displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGeneration of 3D Printed Models\u003c/h3\u003e\n\u003cp\u003eThe printing data of the model were the same as the above-provided data, and 3D Slicer software (version 5.1.0) was also used for three-dimensional reconstruction of anatomical structures such as the scalp and skull (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). The file was then saved in 3D printing format (.stl) and printed via a 3D printing machine (Zhuhai Seine Printing Technology Co., Ltd., model: J401pro) at the Digital Medicine and Additive Manufacturing Engineering Technology Research Center of Jiangmen Central Hospital. The skull part was made of resin material, and the skullcap and skull base bone could be separated to facilitate observation of the internal anatomical structures of the skull and the actual puncture process; the skin part used flexible resin to support repeated puncture operations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eParticipants and evaluation\u003c/h3\u003e\n\u003cp\u003eThirty resident physicians in the Department of Neurosurgery at Jiangmen Central Hospital who had not received similar training between March 2022 and March 2025 were included and randomly divided into two groups (15 people in each group). All participants first received 20 minutes of theoretical teaching, which included identification of the foramen ovale on CT images and skull samples, positioning and line drawing, puncture direction, and operational precautions (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D). After the explanation, the traditional group directly performed puncture operations on the 3D printed model, whereas the study group first performed three-dimensional reconstruction of the model on the computer according to Hartel's anterior localization method, observed the relevant anatomical structures, and formulated a puncture path, which took approximately 15\u0026ndash;20 minutes. HL-2 was subsequently used to perform foramen ovale puncture operations on the 3D-printed model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to the puncture operation experience of the instructing physicians, relevant data on the operations of the two groups were collected. The contents included identification of the foramen ovale on the model, description and operation of puncture positioning, the time taken for the first successful puncture in the two groups, the number of punctures, and the number of puncture failures (puncture time\u0026thinsp;\u0026gt;\u0026thinsp;10 minutes or \u0026ge;\u0026thinsp;3 punctures). Moreover, the confidence of the participants in the two groups in the clinical operations before and after training was also investigated. Finally, a comprehensive scoring method was used to conduct an overall operation evaluation of the participants. Anonymous questionnaires were used to collect feedback from the participants. For puncture positioning and comprehensive scoring, a 5-point scoring standard was adopted, from a minimum of 1 point to a maximum of 5 points; the questionnaire used a 5-point scale from \"very dissatisfied\" to \"very satisfied.\"\u003c/p\u003e\u003cp\u003eThe contents of the puncture positioning score include the following:\u003c/p\u003e\u003cp\u003e ① Description of Hartel's anterior localization method before the operation: 1 point\u003c/p\u003e\u003cp\u003e② Placement of the model position: 1 point\u003c/p\u003e\u003cp\u003e③ Depiction of the positioning line on the model: 1 point\u003c/p\u003e\u003cp\u003e④ Puncture depth: 1 point\u003c/p\u003e\u003cp\u003e⑤ Whether the puncture was successful: 1 point\u003c/p\u003e\u003cp\u003eThe comprehensive operation score was evaluated according to the following items, with a minimum of 1 point and a maximum of 5 points:\u003c/p\u003e\u003cp\u003e① Theoretical knowledge\u003c/p\u003e\u003cp\u003e② Model positioning\u003c/p\u003e\u003cp\u003e③ Puncture operation process\u003c/p\u003e\u003cp\u003e④ Accuracy of the puncture position\u003c/p\u003e\u003cp\u003e⑤ Smoothness of the entire operation process\u003c/p\u003e\n\u003ch3\u003eData Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eBefore performing parametric statistical analysis, the Kolmogorov‒Smirnov test (KS test) was used for normality analysis of the data distribution. Baseline demographic and clinical characteristics are summarized as the mean (mean)\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE) for normally distributed continuous variables, the median (interquartile range, IQR) for nonnormally distributed continuous variables, and the frequency (percentage) for categorical variables. The Mann‒Whitney U test was used to evaluate nonnormally distributed variables. In addition, the comparisons are represented by bar charts, scatter plots, and violin plots of the median and quartile values. The chi-square test or Fisher's exact test was used to compare differences in categorical data. Statistical analysis was performed via the Statistical Package for the Social Sciences (SPSS\u0026reg;, version 25.0; Chicago, Illinois, USA). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCharacteristics of the participants\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the baseline characteristics of the two groups were extremely similar, so randomization of the groups was successfully achieved. The traditional group had an average age of 26.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30 years (13 males and 2 females), and the study group had an average age of 26.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62 years (15 males). There was no significant difference in age or sex (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic characteristics of the two Groups of trained physicians\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraditional group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStudy group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of trainees(n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male/female in %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15/0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.483*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.713#\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e* Chi-square test.\u003c/p\u003e\u003cp\u003e# Independent sample t test.\u003c/p\u003e\n\u003ch3\u003ePuncture Operation Results\u003c/h3\u003e\n\u003cp\u003eThe time required for the foramen ovale in the study group to be successfully punctured for the first time (1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 minutes) and the number of punctures (1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13) were significantly greater than those in the traditional group (6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 minutes and 2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 times, respectively) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). Five physicians (33%) in the traditional group failed to perform the puncture successfully, and six physicians (40%) could not accurately identify the foramen ovale on the printed model. All the physicians in the study group successfully completed the puncture and could accurately identify the foramen ovale on the model (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, in terms of positioning accuracy, the scoring results revealed that the traditional group scored between 3 (IQR, 2\u0026ndash;4), and the comprehensive operation score showed a similar trend: the traditional group scored an average of approximately 4 (IQR, 1\u0026ndash;4), whereas the study group scored 5 (IQR, 5\u0026ndash;5) for both items (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA - B). However, in terms of practical clinical operation confidence, the study group significantly improved after training compared with the traditional group (12/15 vs 5/15), and this difference was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTwo sets of operation results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraditional group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStudy group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst successful puncture time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of punctures (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of puncture failures (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.042\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIdentify the number of foramen ovale (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePuncture localization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(2, 4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(5, 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003csup\u003e△\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperational comprehensive score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(1, 4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(5, 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003csup\u003e△\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfidence in practical operation before training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfidence in practical operation after training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e* Chi-square test.\u003c/p\u003e\u003cp\u003e# Independent sample t test.\u003c/p\u003e\u003cp\u003e△Mann-Whitney U test.\u003c/p\u003e\n\u003ch3\u003eSubjective Questionnaire Results\u003c/h3\u003e\n\u003cp\u003eAs shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA - B, 73.3% of the trained physicians in the traditional group \"strongly agreed\" or \"agreed\" with this training pattern, and 53.3% of the physicians believed that, compared with pure theoretical lectures, this teaching method was better and of great help to them. In the study group, both items reached 100%, 86.7% of which were \"strongly agreed.\" A total of 86.7% of the training physicians in both groups \"strongly agreed\" or \"agreed\" that using 3D printed models could better master anatomical structures and improve practical operation capabilities. A total of 73.3% of the physicians in the study group \"strongly agreed\" that the training pattern of this group could increase their confidence in operations and make them more confident in future work, whereas 26.7% of the physicians in the traditional group \"disagreed\" that the training pattern would improve their confidence. In addition, 93.3% of the doctors in the study group said they hoped that other subjects would also adopt this teaching method, whereas only 53.3% of those in the traditional group did.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePBC has gained widespread clinical application globally because of its simple operation and high degree of safety [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, the current training system still relies mainly on theoretical teaching by senior physicians combined with skull models and lacks practical operation links in real scenarios. In clinical practice, physicians typically rely on fluoroscopy or intraoperative CT for positioning and operation monitoring, which significantly increases the risk of radiation exposure for both doctors and patients. On this basis, this study aims to develop an innovative training pattern to help beginners achieve precise foramen ovale punctures, thereby improving teaching efficiency and quality.\u003c/p\u003e\u003cp\u003eThe iterative increase in computing power has driven significant progress in VR, AR, and MR technologies. As a general term for the abovementioned technologies, XR technology refers to creating a digital environment that combines reality and virtuality through modern high-tech means with a computer as the core, as well as new human‒computer interaction methods, providing an immersive experience of seamless conversion between the virtual and real worlds to users. This technical system is currently rapidly popularizing in the field of neurosurgery and continues to develop. In PBC-related operations, identification of the foramen ovale via preoperative imaging has become the core step in surgical planning. Although thin-slice CT scans provide clearer observations of skull base bones, it is still difficult for some junior physicians to understand the complex three-dimensional skull base structure from two-dimensional images [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. VR technology can reconstruct two-dimensional image data into three-dimensional models, allowing trainees to observe the foramen ovale and surrounding anatomical structures intuitively, measure relevant parameters, and assess puncture difficulty. Although it only displays three-dimensional images on a computer instead of superimposing the reconstructed content onto the real environment, this teaching pattern can arouse trainees' interest in learning and visualize abstract content. Moreover, VR-simulated puncture can improve the success rate, creating familiarity during actual operations to enhance educational effects. As early as 2014, Shakur [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] et al. used a VR simulator developed on the ImmersiveTouch platform for simulated puncture training of the foramen ovale. Junior physicians using VR technology to simulate surgery before the operation help quickly improve surgical skills and shorten the learning curve [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Studies have shown that learning anatomy and surgical paths in a virtual environment may help retain and recall the learning process [\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The data of this study further verify the teaching value of this technology: physicians in the study group who received VR training achieved a 100% accuracy rate in identifying foramen ovale in 3D printed models, whereas 40% of physicians in the traditional group failed to complete this task. In addition, 86.7% of the physicians in the study group \"strongly agreed\" with the VR teaching model, indicating that VR training can better promote beginners' understanding and mastery of the anatomical structures and related knowledge around the surgery.\u003c/p\u003e\u003cp\u003eThe emergence and application of AR and MR technologies have opened new avenues for neurosurgical teaching and personalized, precise surgery. Studies have reported that AR/MR surgical simulators are more suitable for surgical skill training needs than traditional VR simulators are because of their interactive characteristics of virtual-real fusion. Currently, MR surgical simulators, with their advantages of risk-free and low-cost training, are gradually becoming an important part of junior doctor training. By constructing immersive operation scenarios, trainees can experience complex surgical procedures and anatomical structures in a zero-risk environment, which helps junior doctors grow faster and deepen their understanding of complex surgeries. Hooten et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] developed a novel physical‒virtual hybrid simulator for simulating ventricular puncture. Peng et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] successfully constructed a training system for basal ganglia hematoma puncture and anterior ventricular horn puncture by combining 3D printed models with head-mounted mixed reality devices. Inspired by this, this study attempts to couple XR technology with 3D-printed models to construct a simulated surgical scenario that integrates anatomical structures such as the scalp and skull. This model not only addresses the inherent defects of difficult acquisition and cumbersome preservation of cadaver head specimens but also addresses the technical bottleneck of the difficult quantification of puncture accuracy in traditional cadaver head operations through visualization of puncture paths and real-time effect evaluation. 3D-printed models have been proven beneficial for students in surgical education [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Therefore, although the current cost of 3D printing is still high, it remains a worthwhile method (this project is funded by the Key Project of Basic and Applied Basic Research in Jiangmen City). The questionnaire data revealed that 86.7% of the physicians in both the traditional and study groups approved the 3D-printed model, indicating that it helps them better master anatomical structures and improves practical operation capabilities.\u003c/p\u003e\u003cp\u003eThe virtual-hybrid reality surgical training platform constructed by the collaboration of XR technology and 3D-printed models compensates for the deficiency in simulation realism when VR technology or 3D-printed models alone are used. This platform integrates the virtual and real worlds, allowing beginners to complete operations on 1:1 highly simulated anatomical models and obtain visual feedback highly similar to that of human surgery without entering the operating room. Rau et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] used AR technology to guide foramen ovale puncture in a specially designed model and concluded that AR technology greatly improved the accuracy of foramen ovale intubation. The physicians in the study group reported that the most profound feeling during training was that this new immersive teaching method had a strong visual impact, giving them a sense of perspective operation and enhancing participants' confidence in unfamiliar surgical operations [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Studies have shown that, compared with traditional clinical teaching methods, XR technology has significant advantages in clinical skill training [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Compared with traditional methods, which often lack a three-dimensional sense and are relatively monotonous, the XR training platform allows trainees to experience operation processes in a highly realistic environment and intuitively observe relevant anatomical structures from multiple angles. Trainees can also use holographic images to select the best surgical path for repeated practice, thereby improving their understanding of space and direction, mastering operation skills more efficiently, shortening the learning curve, and enabling them to operate more confidently and accurately in future work [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The data show that the study group significantly outperformed the traditional group in terms of first-puncture success time, number of punctures, positioning accuracy, and final scores. After this training method, physicians showed significantly increased confidence in actual surgeries, with 73.3% of training physicians \"strongly agreeing\" that this training method enhanced their operational confidence. In contrast, only 33% of the physicians in the traditional training group \"agreed\" with their training method, considering that it was insufficiently effective. The questionnaire results revealed that 100% of the physicians in the study group believed that this teaching method enhanced their operational confidence, made them more confident in future work, and looked forward to applying this teaching model to other subjects.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eAlthough this study aims to improve the success rate and operational confidence of beginners in foramen ovale puncture during PBC surgery through a new training model, there is still room for optimization in terms of technical simulation accuracy, sample universality, and software-hardware systems: 1. The current training system cannot fully replicate key clinical details of PBC surgery, such as whether the puncture accurately enters Meckel's cave and whether a typical \"pear-shaped expansion\" can be formed after balloon implantation. Scenarios requiring biomechanical feedback and anatomical deformation simulation urgently need to be realized by higher-precision physical simulation devices. 2. The MR technology software used in this study was independently developed by the research team. Similar to mainstream XR technology projects in industry at this stage, scientific research cooperation involves medical-engineering integration. The developed software needs to undergo standardized evaluation and further verification through clinical transformation. 3. The sample of this study included only resident physicians from a single institution, without including clinical physicians at the attending level and above. The differences in the adaptability of physicians with different professional backgrounds and seniority levels to the training model have not been explored. In addition, the extrapolation of single-institution research results is limited, and it is necessary to expand the sample size and carry out multicenter research to comprehensively verify the clinical applicability of this training model. 4. The printing of high-precision anatomical models takes a long time and incurs high material costs, restricting the large-scale application of the training system; the current MR system has technical problems such as registration accuracy fluctuations and image drift, which need to improve interaction stability through hardware iteration and algorithm optimization. Future research needs to continue exploring in the directions of deepening simulation technology, multicenter clinical verification, and software-hardware upgrading to promote the clinical transformation and standardized application of this training pattern.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms the significant feasibility of applying the coupling of XR technology and 3D printed models to the PBC training system. This pattern enables trainees to deeply familiarize themselves with anatomical structures in an immersive environment by constructing virtual-real integrated operation scenarios and stimulating learning initiatives through interactive training. Repeated simulated operations not only accelerate the accumulation of clinical experience and strengthen operational confidence but also achieve a breakthrough in traditional models in terms of teaching efficiency, significantly improving the mastery efficiency of complex procedures through visual anatomical reconstruction and real-time feedback mechanisms.\u003c/p\u003e\u003cp\u003eWith the deepening of interdisciplinary technology integration, the cross-innovation of artificial intelligence, medical imaging, and educational science is reshaping the paradigm of medical training. In the current technical system, the iterative upgrade of device hardware (such as higher-resolution MR headsets and biomechanical simulation models) and the optimization of software algorithms (such as AI-assisted path planning) further enhance the realism and intelligence level of training scenarios. This multitechnology collaborative training model is not only suitable for PBC surgery but also expected to become a universal solution in the field of surgical education.\u003c/p\u003e\u003cp\u003eFuture research will focus on two major directions: ① expanding the applicability of this training model to other neurosurgical procedures (such as brain tumor resection and spinal puncture) and disciplines such as general surgery and orthopedics; and ② promoting its deep integration with the formal standardized training system for resident physicians, verifying teaching effects through multicenter clinical research, and gradually constructing a standardized new medical education ecosystem. This training paradigm, which is based on technological innovation, is opening a broad path for the digital transformation of medical education because of its unique interactivity and repeatability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by Jiangmen Science and Technology Commissioner Research Project​ (grant number: 2024760000310010369) and Jiangmen Central Hospital Education Reform Research Project (grant number: JG202401).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYilong peng，Zhengyuan Xie and Zongyuan Jiang have made substantial contributions to the design of the work , writing the main manuscript text and analysis of data; Shaoai Chen, Xiaohui Li, Jiyong Gu, Jinhong Li, Jiajia Yu and Ronglve Wei have made substantial contributions to the acquisition of data; Zhongjie Shi and Yilong Peng have made substantial contributions to the creation of the new software used in the work; All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSun C, Zheng W, Zhu Q, Du Q, Yu W. 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CURR MED SCI. 2019-02-01; 39 (1): 1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1007/s11596-019-1992-8\u003c/span\u003e\u003cspan address=\"https://doi:10.1007/s11596-019-1992-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Extended reality technology, 3D printing model, Percutaneous balloon compression, Foramen ovale puncture, Medical education training","lastPublishedDoi":"10.21203/rs.3.rs-7556990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7556990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOwing to the complex structure of the skull base, accurate puncture of the foramen ovale in percutaneous balloon compression (PBC) is critical for surgical success. In traditional training models, trainees often struggle to meet the needs of precise training because of the lack of real operational simulation scenarios. This study aims to explore the application feasibility and effectiveness of a new training model that combines extended reality (XR) technology and 3D printing models in precise training for PBC foramen ovale puncture.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThirty participating physicians were randomly divided into a traditional group (n\u0026thinsp;=\u0026thinsp;15) and a study group (n\u0026thinsp;=\u0026thinsp;15). The traditional group received CT image interpretation by attending physicians with PBC experience, anatomical structure explanation of the foramen ovale using skull specimens, teaching of puncture point localization, path planning, and operation key points combined with Hartel's anterior localization method, followed by puncture training on a 1:1 ratio 3D printed model. The study group, on the basis of theoretical explanations, used XR technology for preoperative anatomical structure visualization simulation and puncture path planning, followed by hands-on practice with 3D printed models. Indicators such as the first successful puncture time and number of punctures were recorded, and training effects were comprehensively evaluated through operation scores and questionnaires.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study included 30 resident physicians working and training in the Department of Neurosurgery at Jiangmen Central Hospital.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe first successful puncture time (1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 min) and number of punctures (1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13) in the study group were significantly greater than those in the traditional group (6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 min, 2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 times, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All physicians in the study group successfully completed foramen ovale localization and puncture operations and accurately identified the anatomical structures of the model (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The localization accuracy score (5 points, IQR 5\u0026ndash;5) and comprehensive operation score (5 points, IQR 5\u0026ndash;5) of the study group were significantly higher than those of the traditional group (3 points, IQR 2\u0026ndash;4; 4 points, IQR 1\u0026ndash;4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was no significant difference in clinical operation confidence between the two groups before training (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). After training, the clinical confidence of the physicians in the study group significantly improved (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). One hundred percent of the physicians in the study group recognized that this pattern promoted learning interest and self-confidence.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA training pattern that combines XR technology and 3D printing models can strengthen trainees' spatial understanding of anatomical structures through virtual-real fusion interactive operations, shorten the learning curve, increase trainee precision and psychological confidence in clinical practice, and provide an innovative solution for standardized training involving minimally invasive interventional techniques.\u003c/p\u003e","manuscriptTitle":"Application Effect Study of a New Training Model Combining Extended Reality Technology and 3D Printing Models in Percutaneous Balloon Compression-Accurate Puncture of the Foramen Ovale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 16:21:05","doi":"10.21203/rs.3.rs-7556990/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"294697584334462698575263932564609312929","date":"2025-11-11T16:39:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T11:04:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219287535654090296152864396312346725419","date":"2025-11-06T10:45:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T09:28:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-29T17:57:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T10:39:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-05T15:12:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-10-05T15:08:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"657032d9-2d1f-45c3-ae85-0349a6953afe","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:21:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 16:21:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7556990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7556990","identity":"rs-7556990","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}