Virtual Surgery Simulation Research Articles

Correlation between parameters influencing skill acquisition in cataract surgery simulation

Introduction The EyeSi (Haag-Streit, Manheim, Germany) cataract surgery simulator is the most commonly used virtual reality simulator internationally to train Ophthalmology registrars. It consists of a Cataract Challenge Course (CCC), which is a virtual reality simulation (VRS) of cataract surgery. In this study, we aimed to determine any correlation between the parameters measured on the EyeSi virtual reality cataract surgery simulator and if they can predict the progression of microsurgical skill acquisition and development amongst ophthalmology trainees. Methods Data on the performance of 56 Ophthalmology trainees (training ophthalmic surgeons) at the Royal Victorian Eye and Ear Hospital were analysed from 2018 to 2022. The trainees ranged from first to fourth year of training. Analysed parameters included Initial Task Performance, Time to Gate (the time to reach a threshold score – 50% in this case), and Peak Performance. Relationships between the parameters were analysed with Pearson r, and the significance of the difference between correlations was analysed with the psych package in R. Results The strongest correlation was found between initial and peak performance (r = 0.810), which was significantly greater than the correlation between Initial Task Performance and Time-to-Gate (r = 0.553, p = 0.03). Time-to-Gate was weakly correlated with Peak Performance (r = 0.475). The average total training time was 1123 minutes, ranging from 252 to 2039, and the mean peak CCC score was 442, ranging from 166 to 496. Conclusions Time-to-Gate, Initial Task Performance and Peak Performance are interlinked, indicating that trainees with the highest initial performance remain ahead in ability and can progress through VRS training more rapidly. Data also indicated that the EyeSi platform ultimately prioritizes a wide range of skills over mastery of a few – as participants who spend longer on ‘perfecting’ each stage of the simulator are not truly rewarded points-wise in comparison to those who rush through stages, as rushing through stages grants trainees a far higher overall score for each section (with one’s score ideally being a numeric representation of one’s ‘ability’). Consequently, the authors believe that virtual reality systems play a crucial role in training surgical registrars. However, their scoring systems should focus on skill mastery to facilitate maximal acquisition of skills.

Advancements and Challenges in Virtual Surgery: A Comprehensive Review

Virtual surgery, also known as surgical simulation, has emerged as a transformative technology in the field of medicine, offering innovative solutions for surgical training, preoperative planning, and surgical skill enhancement. This paper provides a comprehensive review of recent advancements, applications, and challenges in virtual surgery. We discuss various virtual surgery techniques, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), and their contributions to surgical education, patient care, and surgical innovation. Additionally, we examine the current state-of-the-art virtual surgery systems, simulation platforms, and surgical training curricula. Furthermore, we explore the challenges and limitations associated with virtual surgery, such as technological constraints, validation and assessment issues, ethical considerations, and cost-effectiveness. Finally, we discuss future directions and opportunities for research and development in virtual surgery, with a focus on addressing existing limitations and advancing the field towards widespread adoption and integration into clinical practice. Keywords: Virtual surgery, surgical simulation, virtual reality, augmented reality, mixed reality, surgical training, preoperative planning, surgical education, surgical innovation.

A Case Study on Orthodontic-Surgical Management of Skeletal Class III Malocclusion in Adults: Clinical Approach and Outcomes

This article presents a case study illustrating the successful management of skeletal Class III malocclusion in an adult through an orthodontic-surgical approach. The clinical case outlines the entire process, from initial assessment to post-treatment outcomes, conducted within our service. Key steps include diagnostic planning using advanced tools such as 3D imaging and virtual surgery simulation. Pre-surgical orthodontic treatment was carefully planned to prepare dental and skeletal structures for surgical correction. Orthognathic surgery was performed precisely according to the established plan, followed by a post-surgical orthodontic phase to finalize occlusion. Short-term and long-term outcomes are evaluated, highlighting significant improvement in facial aesthetics, occlusal function, and post-treatment stability. This case study demonstrates the effectiveness of our integrated approach in managing skeletal Class III malocclusions in adults, underscoring the importance of interdisciplinary collaboration for optimal outcomes

Uniform gradient magnetic field and spatial localization method based on Maxwell coils for virtual surgery simulation

AbstractWith the development of virtual reality technology, simulation surgery has become a low‐risk surgical training method and high‐precision positioning of surgical instruments is required in virtual simulation surgery. In this paper we design and validate a novel electromagnetic positioning method based on a uniform gradient magnetic field. We employ Maxwell coils to generate the uniform gradient magnetic field and propose two positioning algorithms based on magnetic field, namely the linear equation positioning algorithm and the magnetic field fingerprint positioning algorithm. After validating the feasibility of proposed positioning system through simulation, we construct a prototype system and conduct practical experiments. The experimental results demonstrate that the positioning system exhibits excellent accuracy and speed in both simulation and real‐world applications. The positioning accuracy remains consistent and high, showing no significant variation with changes in the positions of surgical instruments.

Smart haptic gloves for virtual reality surgery simulation: a pilot study on external ventricular drain training.

Smart haptic gloves are a new technology emerging in Virtual Reality (VR) with a promise to enhance sensory feedback in VR. This paper presents one of the first attempts to explore its application to surgical training for neurosurgery trainees using VR-based surgery simulators. We develop and evaluate a surgical simulator for External Ventricular Drain Placement (EVD), a common procedure in the field of neurosurgery. Haptic gloves are used in combination with a VR environment to augment the experience of burr hole placement, and flexible catheter manipulation. The simulator was integrated into the training curriculum at the 2022 Canadian Neurosurgery Rookie Bootcamp. Thirty neurosurgery residents used the simulator where objective performance metrics and subjective experience scores were acquired. We provide the details of the simulator development, as well as the user study results and draw conclusions on the benefits added by the haptic gloves and future directions.

Effect of virtual reality training to enhance laparoscopic assistance skills

BackgroundWhile laparoscopic assistance is often entrusted to less experienced individuals, such as residents, medical students, and operating room nurses, it is important to note that they typically receive little to no formal laparoscopic training. This deficiency can lead to poor visibility during minimally invasive surgery, thus increasing the risk of errors. Moreover, operating room nurses and medical students are currently not included as key users in structured laparoscopic training programs.ObjectivesThe aim of this study is to evaluate the laparoscopic skills of OR nurses, clinical medical postgraduate students, and residents before and after undergoing virtual reality training. Additionally, it aimed to compare the differences in the laparoscopic skills among different groups (OR nurses/Students/Residents) both before and after virtual reality training.MethodsOperating room nurses, clinical medical postgraduate students and residents from a tertiary Grade A hospital in China in March 2022 were selected as participants. All participants were required to complete a laparoscopic simulation training course in 6 consecutive weeks. One task from each of the four training modules was selected as an evaluation indicator. A before-and-after self-control study was used to compare the basic laparoscopic skills of participants, and laparoscopic skill competency was compared between the groups of operating room nurses, clinical medical postgraduate students, and residents.ResultsTwenty-seven operating room nurses, 31 clinical medical postgraduate students, and 16 residents were included. The training course scores for the navigation training module, task training module, coordination training module, and surgical skills training module between different groups (operating room nurses/clinical medical postgraduate/residents) before laparoscopic simulation training was statistically significant (p < 0.05). After laparoscopic simulation training, there was no statistically significant difference in the training course scores between the different groups. The surgical level scores before and after the training course were compared between the operating room nurses, clinical medical postgraduate students, and residents and showed significant increases (p < 0.05).ConclusionOur findings show a significant improvement in laparoscopic skills following virtual surgery simulation training across all participant groups. The integration of virtual surgery simulation technology in surgical training holds promise for bridging the gap in laparoscopic skill development among health care professionals.

Could We Expect Postoperative Cup Anteversion after Total Hip Arthroplasty Using Postoperative Plain Anteroposterior and Lateral Radiograph? A Three-Dimensional Experimental Operation Study.

A postoperative radiograph in total hip arthroplasty (THA) is usually obtained to evaluate the inclination and anteversion of the acetabular components. However, there is no gold-standard method for calculating the exact inclination and anteversion of the acetabular components on post-THA radiographs. We aimed to measure the actual anteversion of the acetabular component on postoperative radiographs by obtaining correlation data between the virtual and actual acetabular component positioning using virtual three-dimensional (3D) surgery. A total of 64 hip scans of 32 patients who underwent lower-extremity computed tomography (CT) were retrospectively reviewed. We reconstructed 3D models of the 64 hips using customized computer software (Mimics). Furthermore, to identify the safe zone of acetabular component position in THA, we performed virtual 3D surgery simulations for five anteversion (-10°, 0°, 10°, 20°, and 30°) and five inclination (20°, 30°, 40°, 50°, and 60°) types. We analyzed the acetabular anatomy using 3D models to measure the radiographic, anatomical, and operative anteversion (RA, AA, OA) and inclination (RI, AI, OI) angles. Additionally, we used the Woo-Morrey (WM) method to calculate the anteversion angle in the reconstructed cross-table lateral (CL) radiographs and determined the correlation between these measurements. The safe zone of the acetabular component was visualized on post-THA CL radiographs using the WM method of anteversion measurement based on the different anteversions and inclinations of the acetabular component. The AA, RA, OA, OI, and WM differed significantly between males and females (p value < 0.05). As the anatomical inclination or anteversion increased, the WM anteversion measurements also increased. The radiographic anteversion measurement best matched the WM method of measurement, followed by anatomical and operative methods. The actual anteversion of the acetabular component after THA can be measured on CL radiographs with the WM method using a 3D virtual program, with good reproducibility.

Simulation of soft tissue deformation under physiological motion based on complementary dynamic method

Background and ObjectivePhysiological motions have a significant impact on soft tissue deformation and accuracy of surgical procedures, which is essential for realistic surgical simulation. While existing studies offer accurate simulation of soft tissue deformation, integrating physiological motions into deformation models of soft tissue remains a challenging task. MethodsThis paper introduces a novel deformation model, based on complementary dynamics, to animate soft tissue deformation under physiological motion. The finite element method is incorporated to accurately characterize the elastic behavior of the soft tissue. Mathematical models of physiological motion are utilized and the physiological effects are converted into displacements of a predefined set of handles within the soft tissue mesh. Complementary displacements derived from the inherent dynamics of the soft tissue are calculated, enabling the simulation of physiological motions and elastic behaviors in soft tissue deformation. ResultsExperiments were conducted to evaluate the performance and effectiveness of the proposed method in simulating soft tissue deformation under physiological motion. The simulation results show that the soft tissues exhibit physiological motion that corresponds to the rhythm of arterial pressure fluctuations, heartbeat or respiratory. Furthermore, the presented method exhibits stable performance compared with existing force-based methods. ConclusionsBoth elastic behaviors and physiological motions of soft tissue deformation can be governed by the proposed method. A high degree of realistic visualization is achieved for virtual surgery simulation.

New simulation software to predict postoperative corneal stiffness before laser vision correction.

To develop a new virtual surgery simulation platform to predict postoperative corneal stiffness (Kc mean ) after laser vision correction (LVC) surgery. Narayana Nethralaya Eye Hospital and Sankara Nethralaya, India; Humanitas Clinical and Research Center, Italy. Retrospective observational case series. 529 eyes from 529 patients from 3 eye centers and 10 post-small-incision lenticule extraction (SMILE) ectasia eyes were included. The software (called AcuSimX) derived the anisotropic, fibril, and extracellular matrix biomechanical properties (using finite element calculation) of the cornea using the preoperative Corvis-ST, Pentacam measurement, and inverse finite element method assuming published healthy collagen fibril orientations. Then, the software-computed postoperative Kc mean was adjusted with an artificial intelligence (AI) model (Orange AI) for measurement uncertainties. A decision tree was developed to classify ectasia from normal eyes using the software-computed and preoperative parameters. In the training cohort (n = 371 eyes from 371 patients), the mean absolute error and intraclass correlation coefficient were 6.24 N/m and 0.84 (95% CI, 0.80-0.87), respectively. Similarly, in the test cohort (n = 158 eyes from 158 patients), these were 6.47 N/m and 0.84 (0.78-0.89), respectively. In the 10 ectasia eyes, the measured in vivo (74.01 [70.01-78.01]) and software-computed (74.1 [69.03-79.17]) Kc mean were not statistically different ( P = .96). Although no statistically significant differences in these values were observed between the stable and ectasia groups ( P ≥ .14), the decision tree classification had an area under the receiver operating characteristic curve of 1.0. The new software provided an easy-to-use virtual surgery simulation platform for post-LVC corneal stiffness prediction by clinicians and was assessed in post-SMILE ectasia eyes. Further assessments with ectasia after surgeries are required.

Towards Optimum Mandibular Reconstruction for Dental Occlusal Rehabilitation: From Preoperative Virtual Surgery to Autogenous Particulate Cancellous Bone and Marrow Graft with Custom-Made Titanium Mesh-A Retrospective Study.

The purpose of this retrospective study was to evaluate computer-assisted virtual surgery and the outcomes of mandibular reconstruction using an autogenous particulate cancellous bone and marrow (PCBM) graft combined with a custom-made titanium mesh (TiMesh) using a three-dimensional (3D) printing model. Eighteen consecutive patients were included, and preoperative virtual simulation surgery was performed using digital data. Segmental bone defects showed deviation of the mandible due to displacement of the condyle and segments, unnatural length of the mandibular body, or poorer intermaxillary relationship compared to the marginal bone defect caused by previous operations. These mandibular disharmonies could be simulated, and virtual surgery was performed on a computer with adjustment of displaced mandibular segments, length of the mandibular body, and dental arch with digital bone augmentation. TiMesh was manually pre-bent using a 3D printing model, and PCBM from the iliac crest was grafted with TiMesh. The short-term clinical results were good; reconstruction of the alveolar crest was prosthetically desirable; and minor complications were observed. In conclusion, virtual reconstruction is crucial for treating complex deviated mandibles. Accurate condylar and dental arch positions with an optimum mandibular length are important for prosthetically satisfactory mandibular reconstruction.

Brain Tumour Segmentation Using S-Net and SA-Net

Image segmentation is an application area of computer vision and digital image processing that partitions a digital image into multiple image regions or segments. This process involves the extraction of a set of contours from the input digital image in such a manner that pixels belonging to a region share some common characteristics or computed properties, such as color, texture, or intensity. The application domain of image segmentation is widespread, and includes video surveillance, object detection, traffic control systems, and medical imaging. The application of image segmentation techniques in the field of medical imaging can be further subcategorized into virtual surgery simulation, diagnosis, study of anatomical structures, measurement of tissue volumes, location of tumors and other pathologies. In this study, we have proposed two new Convolutional Neural Network (CNN)-based models: (a) S-Net and (b) Attention S-Net (SA-Net) to perform image segmentation tasks in the field of medical imaging, especially to generate segmentation masks for brain tumours if present in brain Medical Resonance Imaging (MRI). Both proposed models were developed by considering U-Net as the base architecture. The newly proposed models have leveraged the concept of ‘Merge Block’ to infuse both the local and global context, and ‘Attention Block’ to focus on the region of interest having a specific object. Additionally, it uses techniques, such as data augmentation to utilize the available annotated samples more efficiently. The proposed models achieved a Dice Similarity Coefficient (DSC) measure of 0.78 and 0.80 for the High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) datasets, respectively.

Assessment of a high-fidelity, virtual reality-based, manual small-incision cataract surgery simulator: A face and content validity study.

To establish the face and content validity of the HelpMeSee Eye Surgery Simulator - a virtual reality-based cataract surgery simulator for manual small-incision cataract surgery (MSICS). The face and content validity were assessed on the sclero-corneal tunnel construction course. A questionnaire with 11 questions focused on the visual realism, with resemblance to real life surgery, and the training value of the simulator was developed. Thirty-five experienced MSICS surgeons participated in the study. Responses were recorded using a seven-point scoring system. Overall, 74.3% (26/35) of the respondents agreed that the overall visual representation of the eye and the instruments in the simulator were realistic. The task of injecting a visco-elastic through the paracentesis was reported to be the most visually realistic task with a mean score of 5.78 (SD: 1.09; range: 2-7). With regard to content validity, 77.1% (27/35) of the subjects felt agreed that the errors and complications represented throughout the entire tunnel construction module were similar to those encountered in real life; the task of entering the anterior chamber with the keratome had a mean score of 5.54 (SD: 0.98; range 1-7), being rated the highest in that aspect. Overall, 94.3% (33/35) of the subjects agreed that the simulator would be useful in developing hand-eye co-ordination. A similar number of 94.3% (33/35) agreed that based on their experience, they would recommend cataract surgical training on this simulator. The results suggest that the HelpMeSee Eye Surgery Simulator appears to have sufficient face and content validity for cataract surgical training.

Haptic virtual surgery simulation system under field programmable analogue array-based hybrid control

In this paper, a bilateral haptic virtual surgery simulation system under a hybrid controller was studied. An analogue controller realized by a field programmable analogue array (FPAA) was paralleled in the operator robot side, which reduced the impact of controller discretisation on the system. A system stability conditions under hybrid control with multiple-operators were deduced. The stability analysis indicates that the addition of analogue derivative term widens the range of haptic controls gains that satisfy the multiple-users’ stability conditions. Finally, the human’s performance of a stiffness discrimination task was studied in an independently developed minimally invasive surgical (MIS) platform. The experiment results show that, human operators under the hybrid controller achieve the highest task success rates.

Automated 3D Whole-Heart Mesh Reconstruction From 2D Cine MR Slices Using Statistical Shape Model.

Cardiac magnetic resonance (CMR) imaging is the one of the gold standard imaging modalities for the diagnosis and characterization of cardiovascular diseases. The clinical cine protocol of the CMR typically generates high-resolution 2D images of heart tissues in a finite number of separated and independent 2D planes, which are appropriate for the 3D reconstruction of biventricular heart surfaces. However, they are usually inadequate for the whole-heart reconstruction, specifically for both atria. In this regard, the paper presents a novel approach for automated patient-specific 3D whole-heart mesh reconstruction from limited number of 2D cine CMR slices with the help of a statistical shape model (SSM). After extracting the heart contours from 2D cine slices, the SSM is first optimally fitted over the sparse heart contours in 3D space to provide the initial representation of the 3D whole-heart mesh, which is further deformed to minimize the distance from the heart contours for generating the final reconstructed mesh. The reconstruction performance of the proposed approach is evaluated on a cohort of 30 subjects randomly selected from the UK Biobank study, demonstrating the generation of high-quality 3D whole-heart meshes with average contours to surface distance less than the underlying image resolution and the clinical metrics within acceptable ranges reported in previous literature. Clinical Relevance- Automated patient-specific 3D whole-heart mesh reconstruction has numerous applications in car-diac diagnosis and multimodal visualization, including treatment planning, virtual surgery, and biomedical simulations.

Cutting Simulation in Unity 3D Using Position Based Dynamics with Various Refinement Levels

Augmented and Virtual Reality-based surgical simulations have become some of the fastest-developing areas, due to the recent technological advances and changes, in surgical education. Cutting simulation is a crucial part of the virtual surgery simulation in which an incision operation is performed. It is a complex process that includes three main tasks: soft body simulation, collision detection and handling, and topological deformation of the soft body. In this paper, considering the content developer’s convenience, the deformable object simulation, using position-based dynamics (PBD), was applied in the Unity 3D environment. The proposed algorithm for fast collision detection and handling between the cutting tool and the deformable object uses a sweep surface. In case of incision, the algorithm updates the mesh topology by deleting intersected triangles, re-triangulation, and refinement. In the refinement part, the boundary edges threshold was used to match the resolution of new triangles to the existing mesh triangles. Additionally, current research is focused on triangle surface meshes, which help to reduce the computational costs of the topology modifications. It was found that the algorithm can successfully handle arbitrary cuts, keeping the framerate within interactive and, in some cases, in the real-time.

Real‐time biomechanical modelling of the liver using LightGBM model

Accurate and real-time biomechanical modelling of the liver is a major challenge in computer-assisted surgery. Finite element method is often used to predict the deformation of organs for its high modelling accuracy. However, its high computation cost hinders its application in real time, such as virtual surgery simulations. A liver model with biomechanical properties similar to real one is created using finite element method and a data set of the liver deformation with different forces (whose magnitude ranges from 0.1 to 0.5N in omni-direction) acting on different surface points is generated. The mechanical behaviour of liver is simulated in real time by a tree-based LightGBM regression model trained with the generated data set. In comparison with the Random Forest and XGBoost, the LightGBM model achieves the best accuracy with 0.0774mm, 0.0786mm, 0.0801mm in the mean absolute error (MAE) and 0.0591mm, 0.0609 and 0.0622mm in the root mean square error (RMSE) along x, y and z axis, respectively. In addition, it only takes 33ms for the LightGBM model to estimate the deformation of the liver, which is much faster than finite element model (29.91s). These results lay a foundation for the future development of real-time virtual surgery systems of simulating liver deformation during minimally invasive surgeries using our method.

Establishment of Emergency Teaching Model and Optimization of Discrete Dynamic Calculation in Complex Virtual Simulation Environment

Virtual surgery is a typical application of virtual reality technology in the medical field, which can help improve the success rate of surgery and reduce medical costs in various aspects such as medical training, surgery planning, and intraoperative navigation, and is becoming a hot research topic and a frontier subject in the medical field. The establishment of virtual surgery simulation system involves the intersection and penetration of various disciplines, and the research is difficult, and many functional modules are still not perfect. This study focuses on the key technologies in the virtual surgery simulation system, focusing on two core modules, the soft tissue modeling method and the collision detection algorithm, to improve the accuracy of the soft tissue model deformation under the condition of meeting the real-time system. The biomechanical properties of soft tissues are studied, the viscoelastic properties are analyzed, and the viscoelastic theory is used as the basis for soft tissue modeling; the geometric model is established by using a complementary method of surface model and tetrahedral mesh cells, with the surface model covering the outer surface for visual rendering and the tetrahedral mesh cells for skeleton support of the physical model, so that the model has a better visual effect and deformation effect, which enhances the model fidelity that is enhanced by the better visual effect and deformation effect; the soft tissue physical modeling method is summarized and summarized to lay the foundation for soft tissue model optimization. The experimental results show that the bilateral teleoperation system under analog control can give the operator a better haptic sensation (smaller value of input impedance felt by the operator when doing free motion from the robot) while ensuring good positional tracking and force tracking effects. This method solves the problems of collapse distortion and lack of viscoelastic properties of the traditional mass-spring model, and improves the accuracy of model deformation. Based on the improved algorithm, the viscoelastic hybrid filled sphere model of the liver organ is successfully established, which proves that the modeling method is feasible and effective.

Scoring metrics for assessing skills in arthroscopic rotator cuff repair: performance comparison study of novice and expert surgeons.

We aim to develop quantitative performance metrics and a deep learning model to objectively assess surgery skills between the novice and the expert surgeons for arthroscopic rotator cuff surgery. These proposed metrics can be used to give the surgeon an objective and a quantitative self-assessment platform. Ten shoulder arthroscopic rotator cuff surgeries were performed by two novices, and fourteen were performed by two expert surgeons. These surgeries were statistically analyzed. Two existing evaluation systems: Basic Arthroscopic Knee Skill Scoring System (BAKSSS) and the Arthroscopic Surgical Skill Evaluation Tool (ASSET), were used to validate our proposed metrics. In addition, a deep learning-based model called Automated Arthroscopic Video Evaluation Tool (AAVET) was developed toward automating quantitative assessments. The results revealed that novice surgeons used surgical tools approximately 10% less effectively and identified and stopped bleeding less swiftly. Our results showed a notable difference in the performance score between the experts and novices, and our metrics successfully identified these at the task level. Moreover, the F1-scores of each class are found as 78%, 87%, and 77% for classifying cases with no-tool, electrocautery, and shaver tool, respectively. We have constructed quantitative metrics that identified differences in the performances of expert and novice surgeons. Our ultimate goal is to validate metrics further and incorporate these into our virtual rotator cuff surgery simulator (ViRCAST), which has been under development. The initial results from AAVET show that the capability of the toolbox can be extended to create a fully automated performance evaluation platform.

Design and Modeling of PA-10 Virtual Surgery Robot

&lt;div&gt;&lt;p class="4"&gt;This paper introduces the implementation of a virtual laparoscopic surgery simulator assisted by robot. This type of simulator requires three robots: an endoscope robot (Hibou robot in this case) and two surgical robots (Lapbot robot and PA-10 robot in this case). The three robots are operated by the joystick in cholecystectomy and included in the motion deformation algorithm, which modifies the organ to contact the end organ of robot PA-10, so as to make the simulator more realistic. This result provides a basis for laparoscopic surgery simulation using three auxiliary robots, which is an ideal method for training new surgeons.&lt;/p&gt;&lt;/div&gt;

Laboratorial Comparative Study of the Accuracy Between Virtual Planning and Erickson's Platform.

The goal was to perform an orthognathic surgery laboratorial study to evaluate the reproducibility of the resulting movements from virtual surgical planning (VSP), by comparing the measurements obtained from VSP to those obtained from Erickson's platform using the 3D printed surgical guide. Eight patients who had undergone orthognathic surgery were screened and grouped according to maxillary movement and the patient's dentofacial deformity. Three-D virtual surgery simulations were performed, and surgical guides were obtained using Dolphin Imaging 11.9® software. In a semi-adjustable articulator (SAA), the maxilla model was separated from the SAA mounting platform, and the surgical guide was used to reassemble it. Then, the maxilla model was fixed and transferred to Erickson's platform to verify the planned movement. The data were submitted for statistical analysis. The agreement between the methods was excellent, regardless of direction, landmark analyzed, or the amount of movement. The most considerable variation was 0.42mm in the movement amplitude of 6mm, and the highest mean was 0.07mm in the region of the superior right first molar (16). Therefore, regardless the movement of the maxilla, no statistically significant difference was observed between the measurements obtained through VSP and Erickson's platform, demonstrating that both methods are equally accurate and reliable.