Analysis of Artificial Intelligence-assisted Three-Dimensional Landmark Tracing in Orthognathic Surgery Planning: A Comparative Study

Analysis of Artificial Intelligence-assisted Three-Dimensional Landmark Tracing in Orthognathic Surgery Planning: A Comparative Study | 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 Analysis of Artificial Intelligence-assisted Three-Dimensional Landmark Tracing in Orthognathic Surgery Planning: A Comparative Study Sae-Hoon Baek, Soo-Hwan Byun, Yoo-Sung Nam, Hee-Ju Ahn, Sang-Yoon Park, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6167855/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives : Orthognathic surgery aims to correct dentofacial deformities by repositioning the maxillomandibular complex. The advent of digital technology, particularly Virtual Surgical Planning (VSP), has enhanced the precision and efficiency of these procedures. Despite this, comparative analyses of VSP software accuracy remain limited. This study evaluates the accuracy of VSP across multiple software platforms and examines the time-saving potential of Artificial Intelligence (AI)-assisted cephalometry. Mateials and Methods: Fifteen patients were evaluated using three methods: manual tracing with Invivo6, manual tracing with ON3D, and AI-assisted tracing with ON3D. Positional differences were measured at key landmarks (A, ANS, PNS, B, Pog, Me, RU6C, RL6C). Statistical analyses included the Wilcoxon signed-rank test and ANOVA to assess accuracy, while interclass correlation evaluated time measurement reliability among four researchers. Additionally, ANOVA compared time efficiency across methods. Results: Significant positional differences between VSP and Actual Surgical Outcome (ASO) were observed at B, Pog, and Me along the z-axis, and discrepancies were noted at RU6C and RL6C on the y and z axes between Invivo6 and ON3D. AI-assisted cephalometry (Method 3) showed a substantial reduction in analysis time. Conclusions: Mandibular discrepancies between VSP and ASO appear related to postoperative CBCT adjustments, particularly incomplete soft tissue and occlusal adaptation, whereas maxillary discrepancies were minimal (within 2 mm), supporting the accuracy of all methods in the maxilla. Differences at dental landmarks between Invivo6 and ON3D stem from the inclusion of dentition scan data. Clinical Relevance: The use of AI-assisted cephalometry greatly enhanced time efficiency while maintaining accuracy, indicating the potential for AI integration in optimizing orthognathic surgical planning workflows. Virtual surgery planning Computer-aided surgical simulation Artificial intelligence Surgical orthodontics Orthognathic surgery Three-dimensional Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Orthognathic surgery aims to correct dentofacial deformities by repositioning the maxillomandibular complex, addressing functional impairments and aesthetic concerns. Due to the unique skeletal and occlusal features of each patient, the precision of the treatment plan is as crucial as the surgical technique for achieving optimal outcomes. Traditionally, two-dimensional (2D) cephalometric analysis and model surgery have been the primary tools for diagnosis and planning in orthodontics and orthognathic surgery ( 1 ). For instance, Choi et al. reported a mean discrepancy of less than 1 mm between 2D cephalometric surgical planning and the actual outcome in skeletal Class III patients undergoing two-jaw surgery ( 2 ). Despite its utility, 2D analysis falls short in capturing three-dimensional (3D) anatomical details, with errors more likely in complex, extended procedures that demand considerable time in laboratory settings ( 3 – 7 ). To enhance accuracy and efficiency, digital technologies have been developed, aiming to minimize perioperative errors and improve surgical visualization while aligning with surgical treatment objectives (STO). Recent digital advancements have transformed dentistry, especially in orthognathic surgery. Tools such as digital orthodontic study models, 3D analysis, and the integration of cone beam computed tomography (CBCT) with computer-assisted surgical simulation (CASS) have expanded diagnostic and planning capacities ( 8 ). These technologies enable precise virtual simulations of surgical procedures, such as maxillary and mandibular osteotomies, by using computer software to reposition the maxillomandibular complex. While studies validate CASS accuracy ( 9 – 12 ), some have identified clinically significant differences between predicted and actual outcomes, particularly in mandibular sagittal and maxillary vertical positioning ( 9 , 13 ). Nonetheless, technological advances have reduced skepticism surrounding CASS, leading to broader adoption. The establishment of preoperative STOs and the use of customized plates to replicate virtual surgical planning (VSP) during actual surgeries are increasingly common ( 14 , 15 ). Despite extensive research on VSP accuracy, most studies focus on single software programs ( 11 , 16 , 17 ). Comparative evaluations of VSP accuracy across different programs remain limited. In the field of orthodontics and orthognathic surgery, there is a growing demand for fully automated software that enhances the precision and reliability of cephalometric measurements. Manual landmark identification is time-intensive, prompting interest in artificial intelligence (AI) as a promising solution. AI-based platforms like WebCeph (Assemble Circle, South Korea), WeDoCeph (Audax, Slovenia), and Ceph X (ORCA Dental AI, USA) are gaining recognition for their capability to automate cephalometric tracing, superimposition, and treatment simulation, with options for manual landmark calibration ( 13 , 18 , 19 ). These AI-powered platforms have streamlined orthodontic treatment planning, optimizing data collection and analysis for improved time efficiency and accuracy. As orthognathic surgery advances, digital technologies are increasingly influential in enhancing surgical outcomes and reducing planning time. Although VSP accuracy is widely studied, comparative evaluations across different software programs are scarce. This study aims to address this gap by comparing the accuracy of VSP across two programs and assessing efficiency against actual surgical outcomes (ASO). A key aspect of virtual surgery is precise diagnostic landmark identification. Several studies indicate that AI-driven landmark identification can save time while maintaining accuracy ( 20 – 22 ). This study further investigates the time savings associated with AI in diagnosis and examines whether significant differences exist in VSP accuracy and efficiency when AI-assisted cephalometry is employed. The null hypotheses for this study are as follows: 1) There is no difference in VSP accuracy compared to ASO between the two programs, and 2) There is no significant difference in time required for VSP between the programs. Materials and Methods 2.1 Patients This retrospective comparative study included a carefully selected cohort of 15 patients (8 female, 7 male) with a mean age of 22.4 years (± 4.3 years). To reduce variability, patients were selected based on strict inclusion and exclusion criteria rather than increasing the sample size arbitrarily. All patients underwent orthognathic surgery to address either facial asymmetry or skeletal Class III malocclusion (see Table 1 ). The procedures involved Le Fort I osteotomies and bilateral sagittal split ramus osteotomies performed by a single surgeon to maintain procedural consistency. Inclusion criteria: Patients aged 18 years or older with fully completed jaw growth. Availability of preoperative and postoperative CBCT data for orthognathic surgery. Absence of unfavorable fractures, such as angle fractures, during surgery. Exclusion criteria: History of facial trauma or prior orthognathic surgery. Presence of congenital craniofacial anomalies, such as cleft lip or palate. Systemic conditions potentially affecting jaw health, like osteoporosis. Ethical approval was granted by the institutional review board of Hallym University Sacred Heart Hospital (approval No. 2023-08-010-001), and the study adhered to the ethical principles outlined in the Declaration of Helsinki. Table 1 Patient demographics and surgical treatment plan Demographic Value Age (years) 22.4 ± 4.3 Sex (n) - Male - Female 7 8 Diagnosis (n) - Skeletal class III with facial asymmetry - Skeletal class III without facial asymmetry - Facial asymmetry only 3 8 4 Surgical treatment plan (n) - LeFort I osteotomy with posterior impaction - LeFort I osteotomy without posterior impaction - Mandibular advancement - Mandibular setback 10 5 2 13 2.2 Data Acquisition and 3D cephalometry CBCT scans were obtained preoperatively (T0) and postoperatively (T1), both within two weeks of surgery, using the Alphard 3030 (Asahi Inc., Kyoto, Japan) under standardized conditions (80 kV, 5 mA, 17-second exposure time). Images were converted to DICOM format for analysis. CBCT scans for each patient were analyzed using three methods: Method 1 : Virtual Surgical Planning (VSP) and Actual Surgical Outcome (ASO) comparisons using manual tracing in Invivo6 (Anatomage, Santa Clara, CA, USA). Method 2 : VSP and ASO comparisons using manual tracing in ON3D (3DONS, Inc., Seoul, South Korea). Method 3 : VSP and ASO comparisons using AI-assisted landmark identification in ON3D. In Method 1 (Invivo6), all landmarks were manually identified before the virtual surgery. Using the imported dataset in Invivo6, 17 landmarks, including Sella, Basion, bilateral mandibular profiles, and upper/lower molar profiles, were defined (Fig. 1 ). The software automatically generated osteotomy lines for Le Fort I and bilateral sagittal split ramus osteotomy (BSSRO) according to Surgical Treatment Objectives (STO) (Fig. 2 ). Differences in coordinates between VSP and ASO landmarks were calculated. In Methods 2 and 3 (ON3D), the main distinction was between manual and AI-assisted landmark identification. In both methods, head orientation was based on the Frankfort Horizontal (FH) plane after importing data. Five key points (Nasion, bilateral Orbitale, bilateral Porion) were defined manually, irrespective of AI usage for additional landmarks. Unlike Invivo6, ON3D required CBCT image superimposition with an intraoral scanner-acquired dentition scan (Medit i700, v.3.0.3) in STL format (Fig. 3 ). The scan was aligned using landmarks on the first molars and the right central incisor. In ON3D, once the maxilla was repositioned, the mandible auto-adjusted for occlusal fit, whereas in Invivo6, manual mandibular adjustment was necessary (Fig. 4 ). As in Method 1, coordinate differences between VSP and ASO landmarks were calculated. For both Invivo6 and ON3D, the Nasion served as the reference coordinate (0,0,0). Coordinates were expressed in millimeters, with positive directions as follows: left on the X-axis, posterior on the Y-axis, and superior on the Z-axis. The coordinates of eight landmarks common to all 3D surgery modules were compared postoperatively. To evaluate the time efficiency of AI-based diagnostics, time measurements were recorded for two stages: preoperative construction and simulation surgery. Preoperative construction time was defined as the duration for landmark tracing and CBCT data import, while simulation surgery time included the input of STO values, VSP completion, and result extraction. Time was recorded using a stopwatch with interval recording capabilities. 2.3 Definition of Landmarks The landmarks employed in this study were determined using ON3D software version 1.4.0 (Table 2 ). Table 2 Definition of Landmarks used to compare virtual and actual surgery Landmark Definitions A The deepest point between ANS and the upper incisal alveolus ANS The most anterior point of the premaxillary bone in the sagittal plane PNS The most posterior point of the palatine bone in the sagittal plane B The deepest point between Pogonion and lower incisal alveolus Pog Most anterior point of the Symphysis Me The most inferior point on the symphyseal outline RU6C The tip of the mesiobuccal cusp of the maxillary right first molar crown RL6C The tip of the mesiobuccal cusp of the mandibular right first molar crown A, point A; ANS, anterior nasal spine; PNS, posterior nasal spine; B, point B; Pog, pogonion; Me, menton; RU6C, right upper first molar cusp; RL6C, right lower first molar cusp. 2.4 Statistical Analysis The sample size of 15 patients was determined through an a priori power analysis conducted using G*Power 3.1 software (Heinrich Heine University, Düsseldorf, Germany) ( 23 ). This analysis was designed to detect a medium effect size (f = 0.25) with an alpha level of 0.05 and a statistical power (1 - β) of 0.80, which are commonly accepted thresholds in clinical research. The calculation was based on the application of repeated measures ANOVA to compare positional differences across the three tracing methods. This sample size was deemed sufficient to ensure adequate statistical power to detect clinically meaningful differences in both positional accuracy and time efficiency among the methods. All cephalometric measurement data were organized in an Excel spreadsheet and analyzed using SPSS software (version 27; IBM Corp., Armonk, NY, USA). The Wilcoxon signed-rank test was employed to compare the differences between the ASO and the VSP, providing an evaluation of VSP accuracy for each method. To compare accuracy among the three methods, a one-way analysis of variance (ANOVA) was performed. Time efficiency was analyzed by comparing the average time required to complete the VSP for each method across four investigators using one-way ANOVA. Post hoc Bonferroni tests were applied to facilitate multiple comparisons between groups. Inter-examiner reliability was assessed using the interclass correlation coefficient (ICC). Correlation levels were classified as follows: low correlation (ICC 0.75). Results 3.1 Accuracy of VSP across three methods The positional differences between VSP and ASO were analyzed across the three methods. No significant differences were observed in the positional discrepancies among the methods (Table 3 ). Table 3 The Wilcoxon signed rank test results between VSP and ASO at each method. ( * : P value < 0.05) Landmark software Mean Δx [SD] Mean Δy [SD] Mean Δz [SD] P Xv/Xa Yv/Ya Zv/Za A Invivo6 -0.18 [2.03] 0.93 [2.37] 0.21 [2.17] 0.89 0.21 0.93 ON3D (M) 0.01 [0.41] 0.39 [1.43] -0.02 [1.24] 0.93 0.71 0.64 ON3D (A) 0.57 [1.67] 0.46 [1.59] -0.17 [1.22] 0.40 0.64 0.56 ANS Invivo6 -0.21 [2.17] 0.07 [ 1.89] -0.52 [1.91] 0.93 0.93 0.25 ON3D (M) 0.04 [0.51] -0.87 [1.57] -0.73 [1.86] 0.76 0.08 0.10 ON3D (A) -0.02 [0.74] -0.98 [1.92] -0.73 [1.87] 0.98 0.06 0.06 PNS Invivo6 0.21 [2.05] -0.03 [2.94] -0.25 [2.13] 0.60 0.84 0.49 ON3D (M) -0.28 [2.34] -0.36 [1.85] 0.38 [1.56] 0.36 0.33 0.80 ON3D (A) -0.77 [2.98] 0.30 [1.92] 0.65 [1.80] 0.30 0.49 0.42 B Invivo6 -0.62 [3.32] 0.25 [2.78] 0.99 [1.12] 0.52 0.93 0.04* ON3D (M) 0.17 [0.71] -1.12 [2.32] 1.54 [1.55] 0.30 0.21 0.01* ON3D (A) 0.08 [0.80] -1.18 [2.34] 1.53 [1.51] 0.60 0.08 0.01* Pog Invivo6 -0.64 [3.4] -0.34 [2.92] -0.20 [1.99] 0.48 0.45 0.02* ON3D (M) -0.12 [1.57] -1.03 [2.01] 0.36 [0.76] 0.52 0.11 0.04* ON3D (A) -0.12 [1.69] -1.02 [2.01] 1.03 [0.75] 0.72 0.11 0.01* Me Invivo6 -0.69 [3.28] -1.38 [3.78] 1.02 [1.69] 0.45 0.27 0.03* ON3D (M) -0.01 [1.25] -0.99 [2.02] 0.56 [1.43] 0.60 0.09 0.01* ON3D (A) -0.14 [1.22] -0.97 [2.04] 0.83 [1.60] 0.60 0.09 0.03* RU6C Invivo6 0.54 [0.62] 0.19 [1.12] -0.64 [0.99] 0.10 0.52 0.30 ON3D (M) 0.10 [0.82] -0.26 [1.39] 1.07 [1.30] 0.72 0.39 0.55 ON3D (A) 0.07 [0.82] -0.17 [1.59] -0.28 [1.12] 0.45 0.45 0.43 RL6C Invivo6 0.16 [1.12] -0.32 [0.98] 0.17 [1.29] 0.60 0.36 0.72 ON3D (M) 0.18 [1.29] -0.93 [1.56] 0.32 [1.90] 0.92 0.30 0.42 ON3D (A) 0.22 [1.34] -0.78 [1.49] 0.43 [1.99] 0.68 0.55 0.33 Xv/Xa, The difference in the x-coordinate between the virtual surgery and the actual surgical result; Yv/Ya, The difference in the y-coordinate between the virtual surgery and the actual surgical result; Zv/Za, The difference in the z-coordinate between the virtual surgery and the actual surgical result; ON3D(M), ON3D (Manual); ON3D (A), ON3D (AI-digitization). 3.2 Comparative analysis of the positional differences between VSP and ASO across three methods An ANOVA test was conducted to examine potential differences in positional accuracy among the three methods. While no significant differences were noted along the X-axis for any of the eight landmarks, significant differences emerged along the Y and Z axes for the RU6C and RL6C landmarks (Tables 4 – 6 ). Post hoc Bonferroni tests on the Y and Z axes indicated significant differences within Method 3 compared to the other two methods. Table 4 ANOVA results according to Methods 1,2 and 3 at each landmark in the x-axis. software A ANS PNS B Pog Me RU6C RL6C Mean Δx [SD] Invivo -0.18 [2.03] -0.21 [2.17] 0.21 [2.05] -0.62 [3.32] -0.34 [2.92] -0.69 [3.28] 0.54 [0.62] 0.16 [1.12] On3D(M) -0.21 [2.17] 0.04 [0.51] -0.28 [2.34] 0.17 [0.71] -1.03 [2.01] -0.01 [1.25] 0.10 [0.82] 0.18 [1.29] On3D(A) 0.57 [1.67] -0.02 [0.74] -0.77 [2.98] 0.08 [0.80] -1.02 [2.01] -0.14 [1.22] 0.07 [0.82] 0.22 [1.34] P 0.41 0.61 0.09 0.11 0.24 0.63 0.22 0.24 Table 5 ANOVA results according to Methods 1,2, and 3 at each landmark in the y-axis. ( * : P value < 0.05) software A ANS PNS B Pog Me RU6C RL6C Mean Δy [SD] Invivo a 0.93 [2.37] 0.07 [ 1.89] -0.03 [2.94] 0.25 [2.78] -1.38 [3.78] -1.38 [3.78] 0.19 [1.12] -0.32 [0.98] On3D(M) b 0.39 [1.43] -0.87 [1.57] -0.36 [1.85] -1.12 [2.32] -0.99 [2.02] -0.99 [2.02] -0.26 [1.39] -0.93 [1.56] On3D(A) c 0.46 [1.59] -0.98 [1.92] 0.30 [1.92] -1.18 [2.34] -0.97 [2.04] -0.97 [2.04] -0.17 [1.59] -0.78 [1.49] P 0.08 0.63 0.14 0.55 0.44 0.29 0.03* 0.02* Bonferroni c < a,b c < a,b Table 6 ANOVA results according to Methods 1,2, and 3 at each landmark in the z-axis ( * : P value < 0.05) software A ANS PNS B Pog Me RU6C RL6C Mean Δz [SD] Invivo a 0.21 [2.17] -0.52 [1.91] -0.25 [2.13] 0.99 [1.12] -0.20 [1.99] 1.02 [1.69] -0.64 [0.99] 0.17 [1.29] On3D(M) b -0.02 [1.24] -0.73 [1.86] 0.38 [1.56] 1.54 [1.55] 0.36 [0.76] 0.56 [1.43] 1.07 [1.30] 0.32 [1.90] On3D(A) c -0.17 [1.22] -0.73 [1.87] 0.65 [1.80] 1.53 [1.51] 1.03 [0.75] 0.83 [1.60] -0.28 [1.12] 0.43 [1.99] P 0.21 0.31 0.16 0.23 0.55 0.31 0.04* < 0.001* Bonferroni c < a,b c < a,b 3.3 Inter-Examiner Reliability Using Interclass Correlation Coefficients (ICC) The time required to perform VSP was recorded by four researchers across each method, and inter-examiner reliability was assessed using ICC. The ICC values for all measurements were above 0.75, indicating a high level of consistency and sufficient calibration among researchers (Table 7 ). Table 7 Interclass correlation coefficients of each time duration in VSP Interclass correlation coefficients (95% CI) Step Invivo6 ON3D (M) ON3D (A) H1 ~ H4 Preoperative construction 0.80 0.79 0.85 Simulation surgery 0.84 0.88 0.80 total 0.76 0.79 0.84 H1,human 1; H2,human 2; H3, human 3; H4, human 4 3.4 Comparative analysis of average time taken for each step in VSP among three different methods Time efficiency was assessed by comparing the average time taken for each step in VSP across the three methods using ANOVA. Preoperative construction generally required more time than simulation surgery. Notably, Method 3, which employed AI-based landmark identification, had a shorter preoperative construction time compared to Methods 1 and 2, resulting in a reduced total time. Significant differences in time efficiency were observed among the three methods, with post hoc analysis showing no significant differences between the manual methods, whereas the AI-based method demonstrated significant improvements (Table 8 ). Table 8 Analysis of average time for each step in VSP (*: P -value < 0.05) step Invivo6 a ON3D(M) b ON3D(A) c P Bonferroni H1 H2 H3 H4 H1 H2 H3 H4 H1 H2 H3 H4 preoperative construction 10.6 11.12 11.78 11.35 10.58 10.5 10.52 11.15 4.24 4.19 4.18 4.32 0.00* c < a,b [1.55] [0.75] [0.35] [1.87] [1.04] [1.01] [0.90] [0.83] [0.35] [0.40] [0.45] [0.37] Simulation surgery 6.75 6.4 7.27 7.2 6.2 7.05 7.56 7.28 4.42 3.14 4.23 4.31 0.00* c < a,b [1.14] [1.05] [1.35] [1.3] [1.25] [0.23] [1.64] [1.55] [0.39] [0.68] [0.65] [0.62] Total time 17.35 17.52 19.05 18.55 16.78 17.55 18.08 18.43 8.66 7.33 8.41 8.63 0.01* c < a,b [1.25] [2.3] [1.8] [2.9] [2.24] [1.36] [1.62] [1.44] [1.77] [0.66] [0.6] [0.91] Discussion In recent years, the demand for orthognathic surgery has risen, driven by an increased focus on aesthetics and functionality. Historically, orthognathic surgery has depended on two-dimensional (2D) cephalometry for diagnosis, with surgical plans developed manually using tools like facebow transfer. This traditional approach, however, has limitations, including reliance on 2D imaging and susceptibility to errors during manual processes ( 2 ). While various studies have confirmed the accuracy of VSP relative to ASO, few have employed multiple programs for comparative analysis ( 24 – 27 ). This study aimed to evaluate the accuracy of VSP using two distinct programs and to compare the accuracy and time efficiency of VSP methods with and without AI-assisted landmark identification. Our findings revealed significant discrepancies between VSP and ASO along the z-axis at specific mandibular landmarks (B, Pog, and Me) across all three methods (Table 3 ). This aligns with previous studies reporting low concordance between VSP and ASO in the vertical dimension at the B point ( 28 ). The postoperative CBCT scans, taken within two weeks of surgery, likely capture ongoing adaptation in bones, muscles, and soft tissues. During this early recovery period, the occlusion and muscle tone may not yet be fully stable, possibly resulting in discrepancies between the preoperative bite setting and the actual postoperative occlusion. We hypothesize that the observed differences are due to occlusal adaptation and muscle tone adjustments, rather than significant dental movements. In contrast, maxillary landmarks showed no significant differences between VSP and ASO across the three methods. Although significant differences were found at mandibular landmarks along the z-axis, the x and y axes displayed no such discrepancies. The z-axis differences may stem from slight mandibular misalignment during postoperative CBCT scans, potentially resulting in an open bite. This is consistent with the maxilla-first approach in bimaxillary surgery, where the maxillary position is set first, and the mandible is adjusted accordingly to achieve final occlusion ( 29 ). Given the lack of significant discrepancies at maxillary landmarks and the minor (less than 2 mm) differences at mandibular landmarks, our study supports the overall accuracy of VSP across the three methods, with particularly reliable maxillary positioning. This finding aligns with previous research suggesting that discrepancies under 2 mm are clinically insignificant ( 30 , 31 ). The comparative analysis between programs revealed advantages in both precision and time efficiency with ON3D, which supports AI-assisted landmark identification. Although statistical analysis found no significant differences in accuracy among the three methods, discrepancies were observed in the y and z axes at the RU6C and RL6C (maxillary and mandibular right first molars) landmarks. These discrepancies likely result from the dentition scan required in ON3D, which enhances accuracy for dental landmarks but adds an additional step absent in Invivo6. The consistent lack of significant differences along the x-axis across methods supports sufficient accuracy in left-right positioning, as this axis is easier to visualize from the frontal view. However, lateral view analysis (y and z axes) presents greater challenges due to potential overlap with contralateral structures, aligning with previous studies highlighting error susceptibility in lateral views with 2D cephalometry. Post hoc analysis showed significant differences in the y and z axes at RU6C and RL6C, particularly with the AI-assisted method (Method 3). These findings support the use of automated techniques for dental landmark identification, particularly for molar positioning. The absence of significant x-axis differences across methods further supports the reliability of these programs for lateral landmark identification. A notable outcome of this study is the time-saving potential of AI-assisted landmark identification. Method 3, which utilized AI in ON3D, significantly reduced the time required for preoperative construction compared to manual methods (Methods 1 and 2), as evidenced in Table 8 . The time reduction was most pronounced in tasks such as landmark tracing, with minor differences in simulation surgery time across methods, underscoring the efficiency of AI in preoperative planning. In similar studies, AI-assisted landmark tracing demonstrated a 95% reduction in processing time compared to expert manual landmark identification, without a statistically significant difference in accuracy. These findings suggest that AI-based methods can enhance the efficiency of preoperative and postoperative analysis without compromising precision ( 32 ). Among available 3D virtual analysis programs, Dolphin 3D (Chatsworth, CA, USA) is a widely used option, known for combining voxels within a defined area and automatically superimposing VSP and postoperative 3D images. However, it lacks the capability to quantify 3D VSP accuracy. Proplan CMF (Materialise, Leuven, Belgium) functions similarly to ON3D, allowing final occlusion establishment using stone models and CT data, with high demonstrated accuracy between virtual planning and surgical outcomes ( 31 ). Studies by Silva et al. and Meriç P et al. further validated AI-assisted cephalometry, confirming comparable accuracy to manual methods with significantly reduced analysis time ( 33 , 34 ). The study by B. Liu also demonstrated a significant improvement in landmark analysis proficiency and workflow efficiency among senior and junior specialists while maintaining accuracy within the predefined error margins ( 35 ). The findings of this study highlight the potential of AI-powered tools to enhance surgical planning efficiency. However, limitations include a small sample size and a need for broader studies across diverse patient populations with various dentofacial deformities. Future research should focus on expanding AI-based programs to incorporate a wider range of ethnic data, and additional studies are needed to evaluate their clinical performance. Conclusions This study evaluated the outcomes of orthognathic surgery planned with VSP across three methods: AI-assisted landmark tracing and two manual landmark tracing methods, compared against Surgical Treatment Objectives (STO) and ASO. The findings indicate that positional discrepancies at most landmarks were within clinically acceptable limits, supporting the accuracy of all three VSP methods for clinical application. Consequently, the null hypothesis regarding time efficiency (Hypothesis 2) is rejected. The AI-assisted landmark tracing method demonstrated significantly greater efficiency than manual methods, reducing the time required for preoperative planning while maintaining comparable accuracy. This improved efficiency offers the potential to meet the rising demand for orthognathic surgery without compromising treatment quality. Abbreviations AI: Artificial Intelligence STO: Surgical Treatment Objectives CASS: Computer-Assisted Surgical Simulation CBCT: Cone Beam Computed Tomography ICC: Interclass Correlation Coefficients VSP: Virtual Surgical Planning H1: Human 1 H2: Human 2 H3: Human 3 H4: Human 4 ON3D (M): ON3D (Manual) ON3D (A): ON3D (AI-digitization) 3D: Three Dimensional ASO: Actual Surgical Outcome Declarations Ethics approval and consent to participate This retrospective study was approved by the institutional review board of the Hallym University Sacred Heart Hospital (IRB approval No. 2023-08-010-001) and performed in accordance with the Declaration of Helsinki. The subjects read and signed an informed consent. Consent for publication Not applicable. Competing interests The authors declare no conflicts of interest, either directly or indirectly, in the information or products listed in the manuscript. Funding This work was supported by National IT Industry Promotion Agency (NIPA) grant funded by the Korea government (MSIT) (S1402-23-1001, AI Diagnostic Assisted Virtual Surgery and Digital Surgical Guide for Dental Implant Treatment in the Post-Aged Society: A Multicenter Clinical Demonstration). This work was supported by 'Supporting Project to evaluation Domestic Medical Devices in Hospitals' funded by 'Ministry of Health and Welfare (MOHW)' and 'Korea Health Industry Development Institute (KHIDI)'. Author Contribution SHB (Sae-Hoon Baek) and SHB (Soo-Hwan Byun) performed data acquisition and statistical analysis, prepared the figures, and wrote the manuscript. HJA contributed to statistical analysis and interpretation of the data and reviewed the manuscript. SYP, SMY, IYP and SWO contributed to the interpretation of the data, reviewed the manuscript, and participated in the final critical revision. SHB (Soo-Hwan Byun) contributed to the conception and design, coordinated the research project, prepared the figures, and drafted and optimized the manuscript. IM contributed to the interpretation of the data and reviewed the manuscript. BEY contributed to the conception and design, coordinated the research project, prepared the figures, drafted and optimized the manuscript, and operated on the patients. All authors read and approved the final manuscript. Acknowledgement The authors extend their sincere appreciation to Dr. Jun-Young You, Ph.D., Director of the "Gnatho Oral and Maxillofacial Surgery Clinic" in Seoul, Republic of Korea, for providing valuable insights into orthognathic surgery. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Bailey LTJ, Cevidanes LH, Proffit WR (2004) Stability and predictability of orthognathic surgery. Am J Orthod Dentofac Orthop 126(3):273–277 Choi J-Y, Choi J-P, Baek S-H (2009) Surgical accuracy of maxillary repositioning according to type of surgical movement in two-jaw surgery. Angle Orthod 79(2):306–311 Adams GL, Gansky SA, Miller AJ, Harrell WE Jr, Hatcher DC (2004) Comparison between traditional 2-dimensional cephalometry and a 3-dimensional approach on human dry skulls. Am J Orthod Dentofac Orthop 126(4):397–409 Alkhayer A, Piffkó J, Lippold C, Segatto E (2020) Accuracy of virtual planning in orthognathic surgery: a systematic review. Head Face Med 16:1–9 Chen Z, Mo S, Fan X, You Y, Ye G, Zhou N (2021) A meta-analysis and systematic review comparing the effectiveness of traditional and virtual surgical planning for orthognathic surgery: based on randomized clinical trials. J Oral Maxillofac Surg 79(2):471 e1-. e19 Choi J-Y, Song K-G, Baek S-H (2009) Virtual model surgery and wafer fabrication for orthognathic surgery. Int J Oral Maxillofac Surg 38(12):1306–1310 Trpkova B, Major P, Prasad N, Nebbe B (1997) Cephalometric landmarks identification and reproducibility: a meta analysis. Am J Orthod Dentofac Orthop 112(2):165–170 Hong M, Kim M-J, Shin HJ, Cho HJ, Baek S-H (2020) Three-dimensional surgical accuracy between virtually planned and actual surgical movements of the maxilla in two-jaw orthognathic surgery. Korean J Orthod 50(5):293–303 De Riu G, Virdis PI, Meloni SM, Lumbau A, Vaira LA (2018) Accuracy of computer-assisted orthognathic surgery. J Cranio-Maxillofacial Surg 46(2):293–298 Demétrio M-S, Lovisi C-B, Asprino L (2019) Accuracy between virtual surgical planning and actual outcomes in orthognathic surgery by iterative closest point algorithm and color maps: A retrospective cohort study. Medicina oral, patologia oral y cirugia bucal. 24(2):e243 Ritto F, Schmitt A, Pimentel T, Canellas J, Medeiros P (2018) Comparison of the accuracy of maxillary position between conventional model surgery and virtual surgical planning. Int J Oral Maxillofac Surg 47(2):160–166 Zavattero E, Romano M, Gerbino G, Rossi DS, Giannì AB, Ramieri G et al (2019) Evaluation of the accuracy of virtual planning in orthognathic surgery: a morphometric study. J Craniofac Surg 30(4):1214–1220 Tankersley AC, Nimmich MC, Battan A, Griggs JA, Caloss R (2019) Comparison of the planned versus actual jaw movement using splint-based virtual surgical planning: how close are we at achieving the planned outcomes? J Oral Maxillofac Surg 77(8):1675–1680 Figueiredo C, Paranhos L, da Silva R, Herval Á, Blumenberg C, Zanetta-Barbosa D (2021) Accuracy of orthognathic surgery with customized titanium plates–Systematic review. J stomatology oral maxillofacial Surg 122(1):88–97 Fleury CM, Sayyed AA, Baker SB (2022) Custom plates in orthognathic surgery: a single surgeon’s experience and learning curve. J Craniofac Surg 33(7):1976–1981 Park S-Y, Hwang D-S, Song J-M, Kim U-K (2021) Comparison of time and cost between conventional surgical planning and virtual surgical planning in orthognathic surgery in Korea. Maxillofacial Plast Reconstr Surg 43(1):18 Tonin RH, Iwaki Filho L, Yamashita AL, Ferraz FWS, Tolentino ES, Previdelli ITS et al (2020) Accuracy of 3D virtual surgical planning for maxillary positioning and orientation in orthognathic surgery. Orthod Craniofac Res 23(2):229–236 Chung E-J, Yang B-E, Park I-Y, Yi S, On S-W, Kim Y-H et al (2022) Effectiveness of cone-beam computed tomography-generated cephalograms using artificial intelligence cephalometric analysis. Sci Rep 12(1):20585 Kim Y-H, Park J-B, Chang M-S, Ryu J-J, Lim WH, Jung S-K (2021) Influence of the depth of the convolutional neural networks on an artificial intelligence model for diagnosis of orthognathic surgery. J Personalized Med 11(5):356 Bao H, Zhang K, Yu C, Li H, Cao D, Shu H et al (2023) Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health 23(1):191 Bulatova G, Kusnoto B, Grace V, Tsay TP, Avenetti DM, Sanchez FJC (2021) Assessment of automatic cephalometric landmark identification using artificial intelligence. Orthod Craniofac Res 24:37–42 Ye H, Cheng Z, Ungvijanpunya N, Chen W, Cao L, Gou Y (2023) Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification? BMC Oral Health 23(1):467 Faul F, Erdfelder E, Buchner A, Lang A-G (2009) Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behav Res Methods 41(4):1149–1160 Hsu SS-P, Gateno J, Bell RB, Hirsch DL, Markiewicz MR, Teichgraeber JF et al (2013) Accuracy of a computer-aided surgical simulation protocol for orthognathic surgery: a prospective multicenter study. J Oral Maxillofac Surg 71(1):128–142 Stokbro K, Aagaard E, Torkov P, Bell R, Thygesen T (2016) Surgical accuracy of three-dimensional virtual planning: a pilot study of bimaxillary orthognathic procedures including maxillary segmentation. Int J Oral Maxillofac Surg 45(1):8–18 Tucker S, Cevidanes LHS, Styner M, Kim H, Reyes M, Proffit W et al (2010) Comparison of actual surgical outcomes and 3-dimensional surgical simulations. J Oral Maxillofac Surg 68(10):2412–2421 Xia JJ, Gateno J, Teichgraeber JF, Christensen AM, Lasky RE, Lemoine JJ et al (2007) Accuracy of the computer-aided surgical simulation (CASS) system in the treatment of patients with complex craniomaxillofacial deformity: a pilot study. J Oral Maxillofac Surg 65(2):248–254 Kim J-H, Park Y-C, Yu H-S, Kim M-K, Kang S-H, Choi YJ (2017) Accuracy of 3-dimensional virtual surgical simulation combined with digital teeth alignment: a pilot study. J Oral Maxillofac Surg 75(11):2441 e1-. e13 Kim C-S, Lee H (2022) Comparison of actual amount of movement with surgical treatment objective in the orthognathic maxillary repositioning. J Stomatology Oral Maxillofacial Surg 123(3):e85–e9 Ellis IIIE (1999) Bimaxillary surgery using an intermediate splint to position the maxilla. J Oral Maxillofac Surg 57(1):53–56 Sherbel J, Yatabe M, Cevidanes L, Ruellas A, Aronovich S, Ehardt L et al (2023) A method of comparing virtual reality orthognathic surgical predictions and postsurgical treatment outcomes. Virtual Reality 27(4):3089–3099 Blum FMS, Möhlhenrich SC, Raith S, Pankert T, Peters F, Wolf M et al (2023) Evaluation of an artificial intelligence–based algorithm for automated localization of craniofacial landmarks. Clin Oral Invest 27(5):2255–2265 Meriç P, Naoumova J (2020) Web-based fully automated cephalometric analysis: comparisons between app-aided, computerized, and manual tracings. Turkish J Orthod 33(3):142 Silva TP, Hughes MM, Menezes LS, de Melo MFB, PHLd F, Takeshita WM (2022) Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett’s analysis: can we trust a bot to do that? Dentomaxillofacial Radiol 51(6):20200548 Liu B, Liu C, Xiong Y, Zhu H, Zeng W, Guo J et al (2025) Accuracy and Reliability of 3D Cephalometric Landmark Detection with Deep Learning Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6167855","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":426257399,"identity":"f9ee3635-80b1-4595-ba41-90c9f3ac71dd","order_by":0,"name":"Sae-Hoon Baek","email":"","orcid":"","institution":"Hallym University Sacred Heart Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sae-Hoon","middleName":"","lastName":"Baek","suffix":""},{"id":426257400,"identity":"0da18567-71ff-4afa-958f-c707697a9839","order_by":1,"name":"Soo-Hwan Byun","email":"","orcid":"","institution":"Hallym University Sacred Heart Hospital","correspondingAuthor":false,"prefix":"","firstName":"Soo-Hwan","middleName":"","lastName":"Byun","suffix":""},{"id":426257401,"identity":"bda26ef8-22b0-4c28-a7c5-8d480b7c58c6","order_by":2,"name":"Yoo-Sung Nam","email":"","orcid":"","institution":"Hallym University Sacred Heart Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yoo-Sung","middleName":"","lastName":"Nam","suffix":""},{"id":426257402,"identity":"d21edfdf-ea89-48ad-9e83-88438b6d9d41","order_by":3,"name":"Hee-Ju Ahn","email":"","orcid":"","institution":"Hallym University Sacred Heart Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hee-Ju","middleName":"","lastName":"Ahn","suffix":""},{"id":426257403,"identity":"951cbe61-876b-4d72-9e20-ab3120de2d7f","order_by":4,"name":"Sang-Yoon Park","email":"","orcid":"","institution":"Hallym University Sacred Heart Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sang-Yoon","middleName":"","lastName":"Park","suffix":""},{"id":426257406,"identity":"e72ca50a-0121-44db-b3ea-a66e15abbe96","order_by":5,"name":"Sang-Min Yi","email":"","orcid":"","institution":"Hallym University Sacred Heart Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sang-Min","middleName":"","lastName":"Yi","suffix":""},{"id":426257408,"identity":"6d3d7ffd-1911-42e3-a3ee-e3b7845f68ce","order_by":6,"name":"In-Young Park","email":"","orcid":"","institution":"Hallym University","correspondingAuthor":false,"prefix":"","firstName":"In-Young","middleName":"","lastName":"Park","suffix":""},{"id":426257409,"identity":"4e88dc05-db19-4efe-94c7-2888ec59f1dd","order_by":7,"name":"Sung-Woon On","email":"","orcid":"","institution":"Hallym University","correspondingAuthor":false,"prefix":"","firstName":"Sung-Woon","middleName":"","lastName":"On","suffix":""},{"id":426257410,"identity":"9192e151-53bf-4b66-b99d-a99c4a7a0d1c","order_by":8,"name":"Iman Malakuti","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Iman","middleName":"","lastName":"Malakuti","suffix":""},{"id":426257413,"identity":"a0c4d339-d433-4b32-92bd-f93e9dbbabfa","order_by":9,"name":"Byoung-Eun Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBAC9gYg8bGBQYYPKmBAUAvPAQYGxpkNDDxsJGlh5iVNC/vhh49td9jxsElkJzB83MNgbN5ASAtPmrFx7plkoJbcDYwznjGYyRwgoMWeIYdNOreNGayFGehMGwmCDuN/w/7bsq2eFC0SOWzMjG2H4VrMiNDyzFiyt+04DxvP2w0HZxyQMCbCYckPP/xsq5bjZ8/d+ODDARvDGYS0IIBAAsMBBgaCdiAD/gOkqB4Fo2AUjIKRBACTjzScivTgYwAAAABJRU5ErkJggg==","orcid":"","institution":"Hallym University Sacred Heart Hospital","correspondingAuthor":true,"prefix":"","firstName":"Byoung-Eun","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-03-06 07:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6167855/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6167855/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78228935,"identity":"12a3f175-b4a8-4223-8c36-323532c5fe71","added_by":"auto","created_at":"2025-03-11 07:17:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6928386,"visible":true,"origin":"","legend":"\u003cp\u003e3D cephalometry Tracing in Invivo6. Seventeen landmarks and areas were traced for virtual surgery.(a) Frontal view (b) Right lateral view (c) Left lateral view (d) Basal view\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6167855/v1/a327148ea3747bcf3b37a9b0.png"},{"id":78226630,"identity":"5c5d6566-8f2c-40e0-9532-3a9edb703465","added_by":"auto","created_at":"2025-03-11 07:01:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2679673,"visible":true,"origin":"","legend":"\u003cp\u003eCompletion of virtual surgery simulation in Invivo6. The maxilla and mandible surgical cuts automatically generated based on landmark tracing were adjusted according to the STO(a) Frontal view (b) Right lateral view (c) Left lateral view\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6167855/v1/10745412355c55dbc957c1c3.png"},{"id":78226625,"identity":"5516e438-7c40-450c-883c-fa6e29e41e49","added_by":"auto","created_at":"2025-03-11 07:01:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7277782,"visible":true,"origin":"","legend":"\u003cp\u003e3D cephalometry Tracing in ON3D. (a) The initial screen before tracing (b) Frontal view (c) Right lateral view (d) Left lateral view. The basal view is excluded in ON3D landmark tracing\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6167855/v1/4be3e34689dc3c1caa182cad.png"},{"id":78228064,"identity":"0b9b4e54-58c8-41ba-9647-51723be71a32","added_by":"auto","created_at":"2025-03-11 07:09:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2798537,"visible":true,"origin":"","legend":"\u003cp\u003eCompletion of the virtual surgery simulation in ON3D. After relocating the maxilla, the mandible was repositioned using an automatic overlay function. (a) Frontal view (b) Right lateral view (c) Left lateral view\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6167855/v1/ae249c6a40498dbae59bced5.png"},{"id":78230991,"identity":"4e3d1a39-aad4-4953-9d57-205b3de02b6c","added_by":"auto","created_at":"2025-03-11 07:33:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19706207,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6167855/v1/7ee8d673-c1b0-4c90-9681-d987431160f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Artificial Intelligence-assisted Three-Dimensional Landmark Tracing in Orthognathic Surgery Planning: A Comparative Study","fulltext":[{"header":"Background","content":"\u003cp\u003eOrthognathic surgery aims to correct dentofacial deformities by repositioning the maxillomandibular complex, addressing functional impairments and aesthetic concerns. Due to the unique skeletal and occlusal features of each patient, the precision of the treatment plan is as crucial as the surgical technique for achieving optimal outcomes. Traditionally, two-dimensional (2D) cephalometric analysis and model surgery have been the primary tools for diagnosis and planning in orthodontics and orthognathic surgery (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). For instance, Choi et al. reported a mean discrepancy of less than 1 mm between 2D cephalometric surgical planning and the actual outcome in skeletal Class III patients undergoing two-jaw surgery (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite its utility, 2D analysis falls short in capturing three-dimensional (3D) anatomical details, with errors more likely in complex, extended procedures that demand considerable time in laboratory settings (\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo enhance accuracy and efficiency, digital technologies have been developed, aiming to minimize perioperative errors and improve surgical visualization while aligning with surgical treatment objectives (STO). Recent digital advancements have transformed dentistry, especially in orthognathic surgery. Tools such as digital orthodontic study models, 3D analysis, and the integration of cone beam computed tomography (CBCT) with computer-assisted surgical simulation (CASS) have expanded diagnostic and planning capacities (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These technologies enable precise virtual simulations of surgical procedures, such as maxillary and mandibular osteotomies, by using computer software to reposition the maxillomandibular complex. While studies validate CASS accuracy (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), some have identified clinically significant differences between predicted and actual outcomes, particularly in mandibular sagittal and maxillary vertical positioning (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Nonetheless, technological advances have reduced skepticism surrounding CASS, leading to broader adoption.\u003c/p\u003e \u003cp\u003eThe establishment of preoperative STOs and the use of customized plates to replicate virtual surgical planning (VSP) during actual surgeries are increasingly common (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Despite extensive research on VSP accuracy, most studies focus on single software programs (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Comparative evaluations of VSP accuracy across different programs remain limited.\u003c/p\u003e \u003cp\u003eIn the field of orthodontics and orthognathic surgery, there is a growing demand for fully automated software that enhances the precision and reliability of cephalometric measurements. Manual landmark identification is time-intensive, prompting interest in artificial intelligence (AI) as a promising solution. AI-based platforms like WebCeph (Assemble Circle, South Korea), WeDoCeph (Audax, Slovenia), and Ceph X (ORCA Dental AI, USA) are gaining recognition for their capability to automate cephalometric tracing, superimposition, and treatment simulation, with options for manual landmark calibration (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These AI-powered platforms have streamlined orthodontic treatment planning, optimizing data collection and analysis for improved time efficiency and accuracy.\u003c/p\u003e \u003cp\u003eAs orthognathic surgery advances, digital technologies are increasingly influential in enhancing surgical outcomes and reducing planning time. Although VSP accuracy is widely studied, comparative evaluations across different software programs are scarce. This study aims to address this gap by comparing the accuracy of VSP across two programs and assessing efficiency against actual surgical outcomes (ASO).\u003c/p\u003e \u003cp\u003eA key aspect of virtual surgery is precise diagnostic landmark identification. Several studies indicate that AI-driven landmark identification can save time while maintaining accuracy (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This study further investigates the time savings associated with AI in diagnosis and examines whether significant differences exist in VSP accuracy and efficiency when AI-assisted cephalometry is employed. The null hypotheses for this study are as follows: 1) There is no difference in VSP accuracy compared to ASO between the two programs, and 2) There is no significant difference in time required for VSP between the programs.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Patients\u003c/h2\u003e\n \u003cp\u003eThis retrospective comparative study included a carefully selected cohort of 15 patients (8 female, 7 male) with a mean age of 22.4 years (\u0026plusmn;\u0026thinsp;4.3 years). To reduce variability, patients were selected based on strict inclusion and exclusion criteria rather than increasing the sample size arbitrarily. All patients underwent orthognathic surgery to address either facial asymmetry or skeletal Class III malocclusion (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The procedures involved Le Fort I osteotomies and bilateral sagittal split ramus osteotomies performed by a single surgeon to maintain procedural consistency.\u003c/p\u003e\n \u003cp\u003eInclusion criteria:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003ePatients aged 18 years or older with fully completed jaw growth.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAvailability of preoperative and postoperative CBCT data for orthognathic surgery.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAbsence of unfavorable fractures, such as angle fractures, during surgery.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eExclusion criteria:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eHistory of facial trauma or prior orthognathic surgery.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePresence of congenital craniofacial anomalies, such as cleft lip or palate.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSystemic conditions potentially affecting jaw health, like osteoporosis.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eEthical approval\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewas granted by the institutional review board of Hallym University Sacred Heart Hospital (approval No. 2023-08-010-001), and the study adhered to the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient demographics and surgical treatment plan\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (n)\u003c/p\u003e\n \u003cp\u003e- Male\u003c/p\u003e\n \u003cp\u003e- Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis (n)\u003c/p\u003e\n \u003cp\u003e- Skeletal class III with facial asymmetry\u003c/p\u003e\n \u003cp\u003e- Skeletal class III without facial asymmetry\u003c/p\u003e\n \u003cp\u003e- Facial asymmetry only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgical treatment plan (n)\u003c/p\u003e\n \u003cp\u003e- LeFort I osteotomy with posterior impaction\u003c/p\u003e\n \u003cp\u003e- LeFort I osteotomy without posterior impaction\u003c/p\u003e\n \u003cp\u003e- Mandibular advancement\u003c/p\u003e\n \u003cp\u003e- Mandibular setback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2.2 Data Acquisition and 3D cephalometry\u003c/h3\u003e\n\u003cp\u003eCBCT scans were obtained preoperatively (T0) and postoperatively (T1), both within two weeks of surgery, using the Alphard 3030 (Asahi Inc., Kyoto, Japan) under standardized conditions (80 kV, 5 mA, 17-second exposure time). Images were converted to DICOM format for analysis. CBCT scans for each patient were analyzed using three methods:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMethod 1\u003c/strong\u003e: Virtual Surgical Planning (VSP) and Actual Surgical Outcome (ASO) comparisons using manual tracing in Invivo6 (Anatomage, Santa Clara, CA, USA).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMethod 2\u003c/strong\u003e: VSP and ASO comparisons using manual tracing in ON3D (3DONS, Inc., Seoul, South Korea).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMethod 3\u003c/strong\u003e: VSP and ASO comparisons using AI-assisted landmark identification in ON3D.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn \u003cstrong\u003eMethod 1\u003c/strong\u003e (Invivo6), all landmarks were manually identified before the virtual surgery. Using the imported dataset in Invivo6, 17 landmarks, including Sella, Basion, bilateral mandibular profiles, and upper/lower molar profiles, were defined (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The software automatically generated osteotomy lines for Le Fort I and bilateral sagittal split ramus osteotomy (BSSRO) according to Surgical Treatment Objectives (STO) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Differences in coordinates between VSP and ASO landmarks were calculated.\u003c/p\u003e\n\u003cp\u003eIn \u003cstrong\u003eMethods 2 and 3\u003c/strong\u003e (ON3D), the main distinction was between manual and AI-assisted landmark identification. In both methods, head orientation was based on the Frankfort Horizontal (FH) plane after importing data. Five key points (Nasion, bilateral Orbitale, bilateral Porion) were defined manually, irrespective of AI usage for additional landmarks. Unlike Invivo6, ON3D required CBCT image superimposition with an intraoral scanner-acquired dentition scan (Medit i700, v.3.0.3) in STL format (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The scan was aligned using landmarks on the first molars and the right central incisor. In ON3D, once the maxilla was repositioned, the mandible auto-adjusted for occlusal fit, whereas in Invivo6, manual mandibular adjustment was necessary (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). As in Method 1, coordinate differences between VSP and ASO landmarks were calculated.\u003c/p\u003e\n\u003cp\u003eFor both Invivo6 and ON3D, the Nasion served as the reference coordinate (0,0,0). Coordinates were expressed in millimeters, with positive directions as follows: left on the X-axis, posterior on the Y-axis, and superior on the Z-axis. The coordinates of eight landmarks common to all 3D surgery modules were compared postoperatively.\u003c/p\u003e\n\u003cp\u003eTo evaluate the time efficiency of AI-based diagnostics, time measurements were recorded for two stages: preoperative construction and simulation surgery. Preoperative construction time was defined as the duration for landmark tracing and CBCT data import, while simulation surgery time included the input of STO values, VSP completion, and result extraction. Time was recorded using a stopwatch with interval recording capabilities.\u003c/p\u003e\n\u003ch3\u003e2.3 Definition of Landmarks\u003c/h3\u003e\n\u003cp\u003eThe landmarks employed in this study were determined using ON3D software version 1.4.0 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDefinition of Landmarks used to compare virtual and actual surgery\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLandmark\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinitions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe deepest point between ANS and the upper incisal alveolus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eANS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe most anterior point of the premaxillary bone in the sagittal plane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePNS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe most posterior point of the palatine bone in the sagittal plane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe deepest point between Pogonion and lower incisal alveolus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePog\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMost anterior point of the Symphysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe most inferior point on the symphyseal outline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRU6C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe tip of the mesiobuccal cusp of the maxillary right first molar crown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRL6C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe tip of the mesiobuccal cusp of the mandibular right first molar crown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA, point A; ANS, anterior nasal spine; PNS, posterior nasal spine; B, point B; Pog, pogonion; Me, menton; RU6C, right upper first molar cusp; RL6C, right lower first molar cusp.\u003c/p\u003e\n\u003ch3\u003e2.4 Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eThe sample size of 15 patients was determined through an a priori power analysis conducted using G*Power 3.1 software (Heinrich Heine University, D\u0026uuml;sseldorf, Germany) (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e). This analysis was designed to detect a medium effect size (f\u0026thinsp;=\u0026thinsp;0.25) with an alpha level of 0.05 and a statistical power (1 - \u0026beta;) of 0.80, which are commonly accepted thresholds in clinical research. The calculation was based on the application of repeated measures ANOVA to compare positional differences across the three tracing methods. This sample size was deemed sufficient to ensure adequate statistical power to detect clinically meaningful differences in both positional accuracy and time efficiency among the methods. All cephalometric measurement data were organized in an Excel spreadsheet and analyzed using SPSS software (version 27; IBM Corp., Armonk, NY, USA). The Wilcoxon signed-rank test was employed to compare the differences between the ASO and the VSP, providing an evaluation of VSP accuracy for each method. To compare accuracy among the three methods, a one-way analysis of variance (ANOVA) was performed. Time efficiency was analyzed by comparing the average time required to complete the VSP for each method across four investigators using one-way ANOVA. Post hoc Bonferroni tests were applied to facilitate multiple comparisons between groups. Inter-examiner reliability was assessed using the interclass correlation coefficient (ICC). Correlation levels were classified as follows: low correlation (ICC\u0026thinsp;\u0026lt;\u0026thinsp;0.40), moderate correlation (ICC 0.40\u0026ndash;0.75), and high correlation (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.75).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Accuracy of VSP across three methods\u003c/h2\u003e \u003cp\u003eThe positional differences between VSP and ASO were analyzed across the three methods. No significant differences were observed in the positional discrepancies among the methods (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Wilcoxon signed rank test results between VSP and ASO at each method. ( * : \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLandmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esoftware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean Δx [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean Δy\u003c/p\u003e \u003cp\u003e[SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean Δz\u003c/p\u003e \u003cp\u003e[SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXv/Xa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYv/Ya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZv/Za\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvivo6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.18 [2.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 [2.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21 [2.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eON3D (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01 [0.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39 [1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02 [1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eON3D (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 [1.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46 [1.59]\u003c/p\u003e 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.12 [1.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.02 [2.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 [0.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvivo6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.69 [3.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eON3D (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.14 [1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.97 [2.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83 [1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eRU6C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvivo6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54 [0.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 [1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.64 [0.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eON3D (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 [0.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26 [1.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07 [1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eON3D (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 [0.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17 [1.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.28 [1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eRL6C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvivo6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 [1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.32 [0.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17 [1.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eON3D (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18 [1.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.93 [1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32 [1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eON3D (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22 [1.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.78 [1.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43 [1.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\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\u003eXv/Xa, The difference in the x-coordinate between the virtual surgery and the actual surgical result; Yv/Ya, The difference in the y-coordinate between the virtual surgery and the actual surgical result; Zv/Za, The difference in the z-coordinate between the virtual surgery and the actual surgical result; ON3D(M), ON3D (Manual); ON3D (A), ON3D (AI-digitization).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.2 Comparative analysis of the positional differences between VSP and ASO across three methods\u003c/h3\u003e\n\u003cp\u003eAn ANOVA test was conducted to examine potential differences in positional accuracy among the three methods. While no significant differences were noted along the X-axis for any of the eight landmarks, significant differences emerged along the Y and Z axes for the RU6C and RL6C landmarks (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Post hoc Bonferroni tests on the Y and Z axes indicated significant differences within Method 3 compared to the other two methods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA results according to Methods 1,2 and 3 at each landmark in the x-axis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\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\u003esoftware\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePog\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRU6C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRL6C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMean Δx [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvivo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.18 [2.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.21 [2.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21 [2.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.62 [3.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.34 [2.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.69 [3.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.54 [0.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.16 [1.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn3D(M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.21 [2.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04 [0.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.28 [2.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17 [0.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.03 [2.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.01 [1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.10 [0.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.18 [1.29]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn3D(A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57 [1.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.02 [0.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.77 [2.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08 [0.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.02 [2.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.14 [1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.07 [0.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.22 [1.34]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.24\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA results according to Methods 1,2, and 3 at each landmark in the y-axis. ( * : \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\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\u003esoftware\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePog\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRU6C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRL6C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMean Δy\u003c/p\u003e \u003cp\u003e[SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvivo\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93 [2.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07 [ 1.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03 [2.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25 [2.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.38 [3.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.38 [3.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19 [1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.32 [0.98]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn3D(M)\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39 [1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.87 [1.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.36 [1.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.12 [2.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.99 [2.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.99 [2.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.26 [1.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.93 [1.56]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn3D(A)\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46 [1.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.98 [1.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30 [1.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.18 [2.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.97 [2.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.97 [2.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.17 [1.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.78 [1.49]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBonferroni\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ec\u0026thinsp;\u0026lt;\u0026thinsp;a,b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ec\u0026thinsp;\u0026lt;\u0026thinsp;a,b\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA results according to Methods 1,2, and 3 at each landmark in the z-axis ( * : \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\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\u003esoftware\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePog\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRU6C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRL6C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMean Δz\u003c/p\u003e \u003cp\u003e[SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvivo\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21 [2.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.52 [1.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.25 [2.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99 [1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.20 [1.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.02 [1.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.64 [0.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.17 [1.29]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn3D(M)\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.02 [1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.73 [1.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38 [1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.54 [1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36 [0.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.56 [1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.07 [1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.32 [1.90]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn3D(A)\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.17 [1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.73 [1.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65 [1.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.53 [1.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03 [0.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.83 [1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.28 [1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.43 [1.99]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBonferroni\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ec\u0026thinsp;\u0026lt;\u0026thinsp;a,b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ec\u0026thinsp;\u0026lt;\u0026thinsp;a,b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e3.3 Inter-Examiner Reliability Using Interclass Correlation Coefficients (ICC)\u003c/h3\u003e\n\u003cp\u003eThe time required to perform VSP was recorded by four researchers across each method, and inter-examiner reliability was assessed using ICC. The ICC values for all measurements were above 0.75, indicating a high level of consistency and sufficient calibration among researchers (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterclass correlation coefficients of each time duration in VSP\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eInterclass correlation coefficients (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvivo6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eON3D (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eON3D (A)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eH1\u0026thinsp;~\u0026thinsp;H4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative construction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimulation surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\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\u003eH1,human 1; H2,human 2; H3, human 3; H4, human 4\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 Comparative analysis of average time taken for each step in VSP among three different methods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTime efficiency was assessed by comparing the average time taken for each step in VSP across the three methods using ANOVA. Preoperative construction generally required more time than simulation surgery. Notably, Method 3, which employed AI-based landmark identification, had a shorter preoperative construction time compared to Methods 1 and 2, resulting in a reduced total time. Significant differences in time efficiency were observed among the three methods, with post hoc analysis showing no significant differences between the manual methods, whereas the AI-based method demonstrated significant improvements (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of average time for each step in VSP (*: \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003estep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eInvivo6 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eON3D(M) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eON3D(A) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eBonferroni\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epreoperative construction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ec\u0026thinsp;\u0026lt;\u0026thinsp;a,b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[0.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[0.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e[0.37]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSimulation surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ec\u0026thinsp;\u0026lt;\u0026thinsp;a,b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[1.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[0.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[0.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e[0.62]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ec\u0026thinsp;\u0026lt;\u0026thinsp;a,b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[2.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[1.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[2.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[2.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[1.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[1.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[0.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e[0.91]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, the demand for orthognathic surgery has risen, driven by an increased focus on aesthetics and functionality. Historically, orthognathic surgery has depended on two-dimensional (2D) cephalometry for diagnosis, with surgical plans developed manually using tools like facebow transfer. This traditional approach, however, has limitations, including reliance on 2D imaging and susceptibility to errors during manual processes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). While various studies have confirmed the accuracy of VSP relative to ASO, few have employed multiple programs for comparative analysis (\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This study aimed to evaluate the accuracy of VSP using two distinct programs and to compare the accuracy and time efficiency of VSP methods with and without AI-assisted landmark identification.\u003c/p\u003e \u003cp\u003eOur findings revealed significant discrepancies between VSP and ASO along the z-axis at specific mandibular landmarks (B, Pog, and Me) across all three methods (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This aligns with previous studies reporting low concordance between VSP and ASO in the vertical dimension at the B point (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The postoperative CBCT scans, taken within two weeks of surgery, likely capture ongoing adaptation in bones, muscles, and soft tissues. During this early recovery period, the occlusion and muscle tone may not yet be fully stable, possibly resulting in discrepancies between the preoperative bite setting and the actual postoperative occlusion. We hypothesize that the observed differences are due to occlusal adaptation and muscle tone adjustments, rather than significant dental movements.\u003c/p\u003e \u003cp\u003eIn contrast, maxillary landmarks showed no significant differences between VSP and ASO across the three methods. Although significant differences were found at mandibular landmarks along the z-axis, the x and y axes displayed no such discrepancies. The z-axis differences may stem from slight mandibular misalignment during postoperative CBCT scans, potentially resulting in an open bite. This is consistent with the maxilla-first approach in bimaxillary surgery, where the maxillary position is set first, and the mandible is adjusted accordingly to achieve final occlusion (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the lack of significant discrepancies at maxillary landmarks and the minor (less than 2 mm) differences at mandibular landmarks, our study supports the overall accuracy of VSP across the three methods, with particularly reliable maxillary positioning. This finding aligns with previous research suggesting that discrepancies under 2 mm are clinically insignificant (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe comparative analysis between programs revealed advantages in both precision and time efficiency with ON3D, which supports AI-assisted landmark identification. Although statistical analysis found no significant differences in accuracy among the three methods, discrepancies were observed in the y and z axes at the RU6C and RL6C (maxillary and mandibular right first molars) landmarks. These discrepancies likely result from the dentition scan required in ON3D, which enhances accuracy for dental landmarks but adds an additional step absent in Invivo6. The consistent lack of significant differences along the x-axis across methods supports sufficient accuracy in left-right positioning, as this axis is easier to visualize from the frontal view. However, lateral view analysis (y and z axes) presents greater challenges due to potential overlap with contralateral structures, aligning with previous studies highlighting error susceptibility in lateral views with 2D cephalometry.\u003c/p\u003e \u003cp\u003ePost hoc analysis showed significant differences in the y and z axes at RU6C and RL6C, particularly with the AI-assisted method (Method 3). These findings support the use of automated techniques for dental landmark identification, particularly for molar positioning. The absence of significant x-axis differences across methods further supports the reliability of these programs for lateral landmark identification.\u003c/p\u003e \u003cp\u003eA notable outcome of this study is the time-saving potential of AI-assisted landmark identification. Method 3, which utilized AI in ON3D, significantly reduced the time required for preoperative construction compared to manual methods (Methods 1 and 2), as evidenced in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The time reduction was most pronounced in tasks such as landmark tracing, with minor differences in simulation surgery time across methods, underscoring the efficiency of AI in preoperative planning. In similar studies, AI-assisted landmark tracing demonstrated a 95% reduction in processing time compared to expert manual landmark identification, without a statistically significant difference in accuracy. These findings suggest that AI-based methods can enhance the efficiency of preoperative and postoperative analysis without compromising precision (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong available 3D virtual analysis programs, Dolphin 3D (Chatsworth, CA, USA) is a widely used option, known for combining voxels within a defined area and automatically superimposing VSP and postoperative 3D images. However, it lacks the capability to quantify 3D VSP accuracy. Proplan CMF (Materialise, Leuven, Belgium) functions similarly to ON3D, allowing final occlusion establishment using stone models and CT data, with high demonstrated accuracy between virtual planning and surgical outcomes (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Studies by Silva et al. and Meri\u0026ccedil; P et al. further validated AI-assisted cephalometry, confirming comparable accuracy to manual methods with significantly reduced analysis time (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The study by B. Liu also demonstrated a significant improvement in landmark analysis proficiency and workflow efficiency among senior and junior specialists while maintaining accuracy within the predefined error margins (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings of this study highlight the potential of AI-powered tools to enhance surgical planning efficiency. However, limitations include a small sample size and a need for broader studies across diverse patient populations with various dentofacial deformities. Future research should focus on expanding AI-based programs to incorporate a wider range of ethnic data, and additional studies are needed to evaluate their clinical performance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study evaluated the outcomes of orthognathic surgery planned with VSP across three methods: AI-assisted landmark tracing and two manual landmark tracing methods, compared against Surgical Treatment Objectives (STO) and ASO. The findings indicate that positional discrepancies at most landmarks were within clinically acceptable limits, supporting the accuracy of all three VSP methods for clinical application. Consequently, the null hypothesis regarding time efficiency (Hypothesis 2) is rejected. The AI-assisted landmark tracing method demonstrated significantly greater efficiency than manual methods, reducing the time required for preoperative planning while maintaining comparable accuracy. This improved efficiency offers the potential to meet the rising demand for orthognathic surgery without compromising treatment quality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI: Artificial Intelligence\u003c/p\u003e\n\u003cp\u003eSTO: Surgical Treatment Objectives\u003c/p\u003e\n\u003cp\u003eCASS: Computer-Assisted Surgical Simulation\u003c/p\u003e\n\u003cp\u003eCBCT: Cone Beam Computed Tomography\u003c/p\u003e\n\u003cp\u003eICC: Interclass Correlation Coefficients\u003c/p\u003e\n\u003cp\u003eVSP: Virtual Surgical Planning\u003c/p\u003e\n\u003cp\u003eH1: Human 1\u003c/p\u003e\n\u003cp\u003eH2: Human 2\u003c/p\u003e\n\u003cp\u003eH3: Human 3\u003c/p\u003e\n\u003cp\u003eH4: Human 4\u003c/p\u003e\n\u003cp\u003eON3D (M): ON3D (Manual)\u003c/p\u003e\n\u003cp\u003eON3D (A): ON3D (AI-digitization)\u003c/p\u003e\n\u003cp\u003e3D: Three Dimensional\u003c/p\u003e\n\u003cp\u003eASO: Actual Surgical Outcome\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This retrospective study was approved by the institutional review board of the Hallym University Sacred Heart Hospital (IRB approval No. 2023-08-010-001) and performed in accordance with the Declaration of Helsinki. The subjects read and signed an informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no conflicts of interest, either directly or indirectly, in the information or products listed in the manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e This work was supported by National IT Industry Promotion Agency (NIPA) grant funded by the Korea government (MSIT) (S1402-23-1001, AI Diagnostic Assisted Virtual Surgery and Digital Surgical Guide for Dental Implant Treatment in the Post-Aged Society: A Multicenter Clinical Demonstration).\u003c/p\u003e \u003cp\u003eThis work was supported by 'Supporting Project to evaluation Domestic Medical Devices in Hospitals' funded by 'Ministry of Health and Welfare (MOHW)' and 'Korea Health Industry Development Institute (KHIDI)'.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSHB (Sae-Hoon Baek) and SHB (Soo-Hwan Byun) performed data acquisition and statistical analysis, prepared the figures, and wrote the manuscript. HJA contributed to statistical analysis and interpretation of the data and reviewed the manuscript. SYP, SMY, IYP and SWO contributed to the interpretation of the data, reviewed the manuscript, and participated in the final critical revision. SHB (Soo-Hwan Byun) contributed to the conception and design, coordinated the research project, prepared the figures, and drafted and optimized the manuscript. IM contributed to the interpretation of the data and reviewed the manuscript. BEY contributed to the conception and design, coordinated the research project, prepared the figures, drafted and optimized the manuscript, and operated on the patients. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The authors extend their sincere appreciation to Dr. Jun-Young You, Ph.D., Director of the \"Gnatho Oral and Maxillofacial Surgery Clinic\" in Seoul, Republic of Korea, for providing valuable insights into orthognathic surgery.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBailey LTJ, Cevidanes LH, Proffit WR (2004) Stability and predictability of orthognathic surgery. 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BMC Oral Health 23(1):467\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaul F, Erdfelder E, Buchner A, Lang A-G (2009) Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behav Res Methods 41(4):1149\u0026ndash;1160\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu SS-P, Gateno J, Bell RB, Hirsch DL, Markiewicz MR, Teichgraeber JF et al (2013) Accuracy of a computer-aided surgical simulation protocol for orthognathic surgery: a prospective multicenter study. J Oral Maxillofac Surg 71(1):128\u0026ndash;142\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStokbro K, Aagaard E, Torkov P, Bell R, Thygesen T (2016) Surgical accuracy of three-dimensional virtual planning: a pilot study of bimaxillary orthognathic procedures including maxillary segmentation. 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J Oral Maxillofac Surg 75(11):2441 e1-. e13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim C-S, Lee H (2022) Comparison of actual amount of movement with surgical treatment objective in the orthognathic maxillary repositioning. J Stomatology Oral Maxillofacial Surg 123(3):e85\u0026ndash;e9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis IIIE (1999) Bimaxillary surgery using an intermediate splint to position the maxilla. J Oral Maxillofac Surg 57(1):53\u0026ndash;56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSherbel J, Yatabe M, Cevidanes L, Ruellas A, Aronovich S, Ehardt L et al (2023) A method of comparing virtual reality orthognathic surgical predictions and postsurgical treatment outcomes. Virtual Reality 27(4):3089\u0026ndash;3099\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlum FMS, M\u0026ouml;hlhenrich SC, Raith S, Pankert T, Peters F, Wolf M et al (2023) Evaluation of an artificial intelligence\u0026ndash;based algorithm for automated localization of craniofacial landmarks. Clin Oral Invest 27(5):2255\u0026ndash;2265\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeri\u0026ccedil; P, Naoumova J (2020) Web-based fully automated cephalometric analysis: comparisons between app-aided, computerized, and manual tracings. Turkish J Orthod 33(3):142\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva TP, Hughes MM, Menezes LS, de Melo MFB, PHLd F, Takeshita WM (2022) Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett\u0026rsquo;s analysis: can we trust a bot to do that? Dentomaxillofacial Radiol 51(6):20200548\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu B, Liu C, Xiong Y, Zhu H, Zeng W, Guo J et al (2025) Accuracy and Reliability of 3D Cephalometric Landmark Detection with Deep Learning\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Virtual surgery planning, Computer-aided surgical simulation, Artificial intelligence, Surgical orthodontics, Orthognathic surgery, Three-dimensional","lastPublishedDoi":"10.21203/rs.3.rs-6167855/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6167855/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: Orthognathic surgery aims to correct dentofacial deformities by repositioning the maxillomandibular complex. The advent of digital technology, particularly Virtual Surgical Planning (VSP), has enhanced the precision and efficiency of these procedures. Despite this, comparative analyses of VSP software accuracy remain limited. This study evaluates the accuracy of VSP across multiple software platforms and examines the time-saving potential of Artificial Intelligence (AI)-assisted cephalometry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMateials and Methods:\u003c/strong\u003e Fifteen patients were evaluated using three methods: manual tracing with Invivo6, manual tracing with ON3D, and AI-assisted tracing with ON3D. Positional differences were measured at key landmarks (A, ANS, PNS, B, Pog, Me, RU6C, RL6C). Statistical analyses included the Wilcoxon signed-rank test and ANOVA to assess accuracy, while interclass correlation evaluated time measurement reliability among four researchers. Additionally, ANOVA compared time efficiency across methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Significant positional differences between VSP and Actual Surgical Outcome (ASO) were observed at B, Pog, and Me along the z-axis, and discrepancies were noted at RU6C and RL6C on the y and z axes between Invivo6 and ON3D. AI-assisted cephalometry (Method 3) showed a substantial reduction in analysis time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Mandibular discrepancies between VSP and ASO appear related to postoperative CBCT adjustments, particularly incomplete soft tissue and occlusal adaptation, whereas maxillary discrepancies were minimal (within 2 mm), supporting the accuracy of all methods in the maxilla. Differences at dental landmarks between Invivo6 and ON3D stem from the inclusion of dentition scan data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Relevance: \u003c/strong\u003eThe use of AI-assisted cephalometry greatly enhanced time efficiency while maintaining accuracy, indicating the potential for AI integration in optimizing orthognathic surgical planning workflows.\u003c/p\u003e","manuscriptTitle":"Analysis of Artificial Intelligence-assisted Three-Dimensional Landmark Tracing in Orthognathic Surgery Planning: A Comparative Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 07:01:11","doi":"10.21203/rs.3.rs-6167855/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cde85437-4f7b-435a-9e05-37bb2a711b40","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-11T07:01:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-11 07:01:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6167855","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6167855","identity":"rs-6167855","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}