Quantifying ergonomic challenges for urologists operating flexible ureteroscopes through artificial intelligence-based posture estimation toward a paradigm shift to robot-assisted ureteroscopy

Quantifying ergonomic challenges for urologists operating flexible ureteroscopes through artificial intelligence-based posture estimation toward a paradigm shift to robot-assisted ureteroscopy | 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 Quantifying ergonomic challenges for urologists operating flexible ureteroscopes through artificial intelligence-based posture estimation toward a paradigm shift to robot-assisted ureteroscopy Satoshi Kobayashi, Keiji Tsukino, Mikifumi Koura, Tokiyoshi Tanegashima, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8729503/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Purpose: The ergonomic challenges faced by surgeons during flexible ureteroscopy have yet to be thoroughly evaluated using objective methods. However, robot-assisted ureteroscopy has emerged as a promising treatment for urinary stones. The present study quantitatively assesses operative posture during transurethral ureteroscopy via an artificial intelligence-based posture estimation framework, and identifies kinematic differences associated with surgical expertise. Methods: Expert and novice urologists performed standardized flexible ureteroscopic tasks in a simulated transurethral ureteroscopy environment using kidney phantoms containing artificial stones. Upper-body movements were continuously video-recorded and joint coordinates of the shoulders, elbows, and wrists were extracted using an artificial intelligence-based pose estimation system. Kinematic parameters were quantitatively analyzed and compared between groups. Results: Experts completed observation of the entire renal pelvis significantly faster than novices (median 58 vs. 102 seconds, p<0.001). Accumulated travel distance was markedly less in experts for the left shoulder (125 vs. 300 cm, p<0.001), right elbow (324 vs. 1035 cm, p=0.028), left elbow (349 vs. 772 cm, p=0.019), and left wrist (729 vs. 3798 cm, p<0.001). Experts had smaller movement areas at the left shoulder (6.29 vs. 40.5 cm 2 , p<0.001) and right elbow (17.3 vs. 281 cm 2 , p=0.040), and reduced movement ranges across multiple joints. Their angle fluctuation ranges were also narrower for the right shoulder (32.0 vs. 76.3 degrees, p=0.028) and left elbow (70.4 vs. 122.4 degrees, p<0.001). Conclusions: The artificial intelligence-based posture analysis objectively demonstrated ergonomic advantages associated with surgical expertise during flexible ureteroscopy and revealed persistent ergonomic risks to the wrist and elbow. ureteroscopes urinary tract urinary calculi artificial intelligence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The lifetime prevalence of urinary stones is approximately 1 in 7 men and 1 in 15 women [ 1 ]. Despite preventive strategies, recurrence rates remain high, reaching 45% at 5 years and 60% at 10 years [ 2 ]. Transurethral ureteroscopy (TUL) for urinary stones, utilizing a flexible ureteroscopy (fURS) device, has achieved high stone-free rates and an acceptable complication profile. However, recurrence rates can be up to 2.25 times higher depending on stone location [ 3 ]. As the incidence and complexity of stone disease increase due to increased obesity and metabolic complications, the number of TUL procedures being carried out continues to rise at a rate of 6–7% per year, placing greater demands on endourologists and operating-room resources [ 4 ]. Increased surgeon workload associated with TUL has been suggested as a long-term cause of carpal tunnel syndrome and elbow arthritis attributed to repetitive wrist and arm movements in a static posture. These symptoms have been linked to burnout and reduced productivity [ 5 ]. However, no studies have examined the relationship between posture during ureteroscopy and functional impairment [ 6 , 7 ]. Although the Rapid Entire Body Assessment and Rapid Upper Limb Assessment scoring systems are considered optimal tools for evaluating ergonomics during ureteroscopy, these indices rely on observational assessment and are limited in the ergonomic evaluation of fURS by their subjective nature and the use of time sampling [ 8 , 9 ]. Robot-assisted ureteroscopy (rURS) is an emerging technology in stone treatment that potentially heralds a paradigm shift away from fURS. Its feasibility is currently under evaluation [ 10 ]. rURS has been introduced as an alternative to percutaneous nephrolithotripsy due to its potential to provide improved operability and enhanced access to each renal calyx, as compared to fURS [ 11 ]. Several studies have demonstrated that rURS reduces physical fatigue and musculoskeletal strain among endourologists, relative to fURS [ 12 – 15 ]. This is hypothesized to include the significant alleviation of repetitive wrist movements and awkward arm positioning [ 16 ]. However, few existing studies have analyzed endourologists’ posture, making it challenging objectively to identify unmet ergonomic needs associated with traditional fURS. Consequently, the ergonomic benefits of rURS for endourologists remain to be comprehensively evaluated. In response to these gaps, this study developed an artificial intelligence (AI)-based pose estimation pipeline specifically for TUL to objectively identify ergonomic issues associated with fURS. These issues were then evaluated by analyzing endourologists’ posture during simulated TUL procedures. Materials and Methods Statement of Ethics All procedures in this study were performed in accordance with the 1964 Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board (approval no. 28062) of Kyushu University. Moreover, informed consent was obtained from all the participants, and they were provided with clear opportunities to opt out, respecting their autonomy and right to choose. Kidney and Bone Phantom Models A segmentation tool (3D Slicer version 4.11, Brigham and Women’s Hospital, Boston, MA, USA) was used to annotate the spine, ribs, renal pelvis, calyces, and upper ureter for each computed tomography scan slice (Fig. 1 -a). Artificial supports for the kidney model were designed within the annotation data and placed on the spine–rib model [ 17 ]. The annotations were converted into a Standard Triangulated Language file. The bone model was fabricated using three-dimensional (3D) printing with acrylonitrile-butadiene-styrene resin. The kidney model was produced by casting a kidney mold and injecting silicone resin. A stone resembling a urinary stone was placed in each renal calyx within the kidney model. A uroscope-specific endoscopic video image processor (DVM Model B, Tokyo Kikai Boeki Corporation, Japan) and a single-use ureteroscope (Uroscope, Tokyo Kikai Boeki Corporation, Japan) were utilized. The ureteroscope featured an 8.4 Fr diameter and 285° upper and lower curvature angles. A 300W xenon short-arc lamp with a color temperature of 5800 K served as the fURS light source (Fig. 1 -b). Kidney and bone phantom models were arranged and a 12/14 Fr ureteral access sheath (Coloplast, Denmark) was inserted into the upper ureter of the kidney model to simulate the fURS procedure in real TUL (Fig. 1 -c). Participant Eligibility Expert participants were defined as those who hold board-certified specialists qualifications by the Japanese Urological Association. Novice participants were residents with less than 1 year of experience as urologists and without specialist qualifications. All participants provided informed consent and were briefed on the validation task and evaluation method prior to the experiment. Experiment Procedures Each subject faced the phantom model in the default posture prior to the task (Fig. 2 -a), operated the fURS device to reach the renal pelvis of the phantom model and accessed the upper, middle, and lower renal calyces sequentially, starting from the top. Targets placed within each calyx were visually observed in the center of the endoscopic image. Dataset Preparation To measure the positions of the subjects’ shoulder, elbow, and wrist joints, their movements during the fURS procedure were video-recorded at 30 frames per second. A webcam (BSW300MBK, BUFFALO, 1920 × 1080 px) captured the video throughout the experimental procedure. AI-Based Pose Estimation Architecture The MediaPipe Pose Landmark model was used to obtain two-dimensional (2D) estimates of the subjects’ joint coordinates for each video frame. The coordinates of 22 out of 33 full-body landmarks were analyzed and the estimated landmarks were superimposed on the video (Fig. 2 -b). For each frame, the landmark coordinates of the shoulders, elbows, and wrists ([shoulder_x, shoulder_y], [elbow_x, elbow_y], [wrist_x, wrist_y]) were converted to a Comma-Separated Values file for subsequent metric analysis. Analysis Metrics Accumulated travel distance (ATD) of each landmark The distance each landmark moved in each image frame was measured, and the cumulative distance was calculated as ADT in Fig. 3 -a. Movement speed (MS) of each landmark The MS of each anatomical landmark (joint) was calculated for each frame. Movement area (MA) of each landmark (Fig. 3 -b) From the 2D video images, the range of motion for anatomical landmarks (joints) was calculated as the MA. Assuming that the points (coordinates) that make up the trajectory—meaning the sequential path traced by the anatomical landmark (joint) positions—the area of the trajectory when considered as a closed figure was calculated using the Shoelace formula. Movement range (MR) of each landmark (Fig. 3 -c) The area of the smallest convex polygon connecting the outermost points to which the landmark (joint) moved was calculated as the MR using the convex hull from 2D video images. Angle fluctuation range (AFR) of each landmark The angle of the vector from one joint to the other two points was calculated using the arctangent function. The coordinates of the shoulder, elbow, and wrist were utilized, and the difference between the two angles was expressed in degrees. Statistical Analysis Subjects were categorized into expert and novice groups and the fURS device operation was assessed respectively. The ATD, MS, MA, MR, and AFR were reported as medians with interquartile ranges. Data with a normal distribution and a p-value of 0.05 or greater in the Shapiro–Wilk test were analyzed using t-tests. Data not following a normal distribution and with a p-value of less than 0.05 were analyzed using the Wilcoxon test. All analyses were conducted using JMP Pro version 19.0.0 for Mac (SAS Institute Inc., Cary, NC, USA). Statistical significance was defined as p < 0.05. All p-values were evaluated using two-side tests. Results Baseline urologist’s characteristics Table 1 showed the baseline characteristics of the urologists who participated in this study. All subjects were right-handed and operated the fURS device with their right hand. Experts had a significantly higher number of performing fURS in lifetime (Expert group vs. Novice group: 100 vs. 10 cases, p < 0.001). Table 1 Baseline urologists’ characteristics Median, (IQR) Expert group n = 7 Novice group n = 5 p values Age, years 36 (36–38) 28 (28–30) < 0.001 Male, n [%] 7 [100] 5 [100] 1.00 Right handed, n [%] 7 [100] 5 [100] 1.00 Years of experience as a doctor 11 (9–11) 3 (3–3) < 0.001 Number of Board Certified Specialists 7 0 < 0.001 Supervising physician qualification 1 0 0.429 Number of fURS performed in lifetime 100 (100–100) 10 (7–13) < 0.001 100 cases 6 0 fURS : flexible ureteroscopy; IQR : Interquartile Range Performing fURS All participants were able to complete the task. Experts required significantly less time to complete observation of the entire renal pelvis using the fURS device compared to novices (median: 58 vs. 102 seconds, p < 0.001). Metrics by AI-based pose estimation In experts, the left shoulder joint exhibited a smaller MA (6.29 vs. 40.5 cm², p < 0.001) and range (23.4 vs. 148 cm², p < 0.001), while the right shoulder also demonstrated a reduced MR (50.7 vs. 318 cm², p = 0.040) (Fig. 5 -a, d). On the other hand, there was no significant difference in the MA of the right shoulder joint (16.2 vs. 41.6 cm 2 , p = 0.242). The right elbow joint showed both a smaller MA (17.3 vs. 281 cm², p = 0.040) and MR (91.6 vs. 1112 cm², p = 0.019). The left elbow joint also had a reduced MR (780 vs. 187 cm², p = 0.013) (Fig. 5 -b, e). There was no significant difference in MA values at the left elbow joint (16.0 vs. 32.0 cm 2 , p = 0.661). The left wrist’s MA was smaller in experts (303 vs. 1188 cm², p = 0.019) (Fig. 5 -c, f). There were no significant differences in the MA in either wrist joint (135 vs. 32.5 cm², p = 0.558; 48.7 vs. 94.6 cm², p = 0.188) or in the MR in the right wrist joint (540 vs. 1025 cm², p = 0.107). Discussion This study developed and applied an AI-based pose-estimation pipeline to objectively evaluate ergonomic challenges encountered by surgeons using a fURS during TUL. Analysis of detailed kinematic parameters during simulated TUL procedures revealed clear and quantifiable differences in operative posture and movement patterns between expert and novice endourologists. Experts observed the entire renal pelvis significantly faster than novices, indicating greater procedural efficiency. More importantly, experts consistently exhibited reduced ATD, smaller MA, and narrower MR across multiple upper-body joints, particularly in the shoulders, elbows, and wrists. Our findings suggest that expert surgeons adopt a stable movement strategy, characterized by rotating the fURS device primarily around the elbow while minimizing unnecessary shoulder and wrist excursions. This approach resulted in shorter ATD for both elbows and the left wrist, as well as significantly restricted MA and angular fluctuation ranges in the shoulder and elbow joints. In contrast, novices demonstrated excessive and dispersed joint movements, likely reflecting compensatory postures and inefficient motor control during fURS manipulation. These differences were not consistently captured in the MS alone, underscoring the importance of comprehensive kinematic analysis as opposed to reliance on isolated metrics. This study provides objective evidence linking surgeon expertise to ergonomic efficiency, which addresses a critical gap in the literature, previous studies having relied largely on subjective observational tools such as the Rapid Entire Body Assessment and Rapid Upper Limb Assessment scoring systems [ 8 , 9 ]. By visualizing and quantifying posture-related functional impairment, our AI-based framework enables precise identification of ergonomic stressors inherent to fURS [ 18 , 19 ]. These results not only enhance understanding of skill-dependent ergonomic adaptation but also provide a foundation for targeted training strategies and the objective evaluation of the ergonomic advantages of emerging technologies such as rURS [ 16 ]. The study finds that right-handed expert urologists performed fURS efficiently; however, although the ATD, MA, and MR of the right wrist did not change, rotational movement, dorsiflexion, and extension of the right wrist likely increased. As increased wrist rotation or extension is directly associated with carpal tunnel syndrome, these findings indicate that technical proficiency does not eliminate the long-term ergonomic risks associated with fURS [ 20 ]. For the non-dominant hand, a reduction in elbow flexion angle during fURS resulted in increased forearm flexion, thereby elevating the risk of elbow arthritis [ 21 ]. Consequently, the risk of elbow arthritis remains for long-term fURS procedures [ 22 ]. rURS is performed using a gamepad and does not require the upper-limb movements characteristic of fURS, potentially reducing ergonomic risks [ 23 ]. Therefore, this study objectively demonstrates that fURS poses risks of carpal tunnel syndrome and elbow arthritis among urologists, while also identifying rURS as a feasible ergonomic and clinical alternative [ 16 ]. The rURS has emerged as an advanced modality for urinary stone management, with the goal of enhancing clinical outcomes. The initial prospective clinical evaluation of the ILY® rURS platform in the management of kidney stones, conducted in 29 patients with a cumulative total of 45 stones, demonstrated both safety and efficacy, the platform achieving a stone-free rate of 93.6% and only minimal Clavien–Dindo grade I complications [ 14 ]. A meta-analysis of studies evaluating the ILY® rURS manipulator platform and the MONARCH™ platform reported stone-free rates of approximately 86.0–87.4%, which are comparable to those achieved with traditional fURS, as well as ergonomic advantages such as reduced surgeon fatigue and lower radiation exposure [ 16 ]. Nevertheless, although rURS offers ergonomic benefits, the underlying ergonomic challenges of fURS—such as awkward hand positioning, extended periods of gripping, and strain from prolonged procedures—have not been clearly delineated in the literature. The present study contributes to the expanding body of evidence supporting the ergonomic advantages of rURS. However, several limitations should be acknowledged. First, the analysis was performed in a simulated TUL environment rather than during live clinical procedures. Simulation provides standardized conditions and precise motion capture [ 24 ]. However, it fails fully to replicate the cognitive demands, time constraints, and anatomical variability of real surgical settings; thus, surgeons’ observed posture and movement patterns may not accurately reflect actual patient care [ 25 ]. Previous studies have demonstrated high accuracy in tracking upper-limb trajectories using MediaPipe. Building on these findings, it is necessary to conduct a comparative analysis during TUL to determine how MediaPipe's tracking accuracy compares with optical motion capture using optical markers, which is considered the gold standard [ 26 ]. Second, the small size of the sample, which was taken from a single institution, may restrict generalizability. A large volume of time-series data frames was collected from continuous surgical movements, thereby enhancing statistical reliability [ 27 ]. Third, the study focuses only on upper-body kinematics via 2D pose estimation. Although the AI-based pipeline enables objective assessment of joint movement, it does not evaluate muscle activation, force exertion, or static muscle loading, all of which contribute to musculoskeletal fatigue and injury.[ 28 ] However, increased ATD indicates repetitive stress on joints and muscles, a known risk factor in the development of carpal tunnel syndrome. Similarly, understanding the causal link between AFR and anatomical stress in elbow arthritis requires measuring AFR's effects on joint dynamics. Employing wearable sensors, electromyography, and force-measuring devices to monitor wrist joint angle, muscle activation, and force production directly connect observed ergonomic risks to musculoskeletal fatigue and injury [ 29 ]. Future studies should use wearable sensors, electromyography, and force-measurement devices to monitor wrist joint angles, muscle activation, and force exertion, enabling a comprehensive assessment of ergonomic risks contributing to musculoskeletal fatigue and injury [ 30 ]. Fourth, the ergonomic evaluation relates only to fURS procedures, with no comparisons to robot-assisted or other endourological modalities [ 21 ]. The ergonomic advantages of alternative platforms cannot therefore be assessed. Finally, the cross-sectional design prevents causal inferences regarding observed movement patterns and long-term musculoskeletal disorders [31]. Future research should include longitudinal studies that integrate clinical outcomes, surgeon-reported symptoms, and intraoperative biomechanical data to clarify whether identified ergonomic differences reduce fatigue and injury risk. Despite these limitations, the study has several strengths. It objectively demonstrates the ergonomic challenges of fURS through an AI-based pose estimation pipeline. The findings suggest that transitioning to rURS is feasible from an ergonomic perspective. Conclusion This study has identified ergonomic challenges associated with operating fURS devices using a specialized AI-based pose-estimation pipeline. Ergonomic performance was assessed objectively using the pipeline to analyze surgical hand and body positioning. The results demonstrate that rURS is ergonomically feasible for treating the upper urinary tract. Additionally, the AI-based framework provides objective feedback on ergonomic techniques. Abbreviations AFR: Angle fluctuation range ATD: Accumulated travel distance fURS: flexible ureteroscopy MA: Movement area MR: Movement range MS: movement speed rURS: robot-assisted ureteroscopy TUL: Transurethral ureteroscopy 2D: two-dimensional 3D: three-dimensional Declarations Acknowledgment This study was supported by JSPS KAKENHI Grant Numbers JP23K19219 and 25K15937, Japanese Foundation for Research and Promotion of Endoscopy Grant, and Japanese Society of Endourology and Robotics Robot-assisted Surgery Research Grant A (2024). Research Involving Human Participants and / or Animals Not applicable. Data Availability The data that support this study’s findings are available from the corresponding author [Satoshi Kobayashi] upon reasonable request. Compliance with Ethical Standards Disclosure of potential conflicts of interest: The authors hereby state that they have no relevant financial interests to declare. Research involving human participants and / or animals: Not applicable. Informed consent: Informed consent was obtained from all the participants, and they were provided with clear opportunities to opt out, respecting their autonomy and right to choose. Funding: This study was supported by JSPS KAKENHI Grant Numbers JP23K19219 and 25K15937, Japanese Foundation for Research and Promotion of Endoscopy Grant (2025), and Japanese Society of Endourology and Robotics Robot-assisted Surgery Research Grant A (2024). Author Contributions All authors contributed to writing, review, and editing. Satoshi Kobayashi was involved in the design and implementation of the study, model creation, data collection and interpretation, and drafting of the paper. Keiji Tsukino provided technical support for the AI-based pose estimation and analyzed some of the data. Mikifumi Koura contributed technical support and data collection. Masaki Shiota and Masatoshi Eto provided guidance and supervision, respectively, and ensured the smooth progress of the research. This paper is the culmination of our joint efforts and all authors have read and approved the final version. Satoshi Kobayashi: Protocol/project development, Data collection or management, Data analysis, Manuscript writing/editing Keiji Tsukino: project development, Data collection, Data analysis Mikifumi Koura: Data collection, Manuscript writing/editing Tokiyoshi Tanegashi: Manuscript writing/editing Shigehiro Tsukahara: Manuscript writing/editing Takashi Matsumoto: Writing - review & editing, Writing - original draft Masaki Shiota: : Writing - review & editing, Writing - original draft Masatoshi Eto: Supervision, Writing - : Writing - review & editing, Writing - original draft, management References Liu Y, Chen Y, Liao B, Luo D, Wang K, Li H, Zeng G. Epidemiology of urolithiasis in Asia. Asian J Urol . 2018;5(4):205–214. doi: 10.1016/j.ajur.2018.08.007. Qian X, Wan J, Xu J, Liu C, Zhong M, Zhang J, Zhang Y, Wang S. Epidemiological Trends of Urolithiasis at the Global, Regional, and National Levels: A Population-Based Study. Int J Clin Pract . 2022;2022:6807203. doi: 10.1155/2022/6807203. Khan SR, Pearle MS, Robertson WG, Gambaro G, Canales BK, Doizi S, Traxer O, Tiselius HG. Kidney stones. Nat Rev Dis Primers. 2016;2:1-23. https://doi.org/doi:10.1038/nrdp.2016.8 Bhanot R, Jones P, Somani B. Minimally Invasive Surgery for the Treatment of Ureteric Stones - State-of-the-Art Review. Res Rep Urol. 2021:6;13:227-236. doi: 10.2147/RRU.S311010. Gabrielson AT, Clifton MM, Pavlovich CP, Biles MJ, Huang M, Agnew J, Pierorazio PM, Matlaga BR, Bajic P, Schwen ZR. Surgical ergonomics for urologists: a practical guide. Nat Rev Urol . 2021:18(3):160-169. doi: 10.1038/s41585-020-00414-4. Wright HC, Gheordunescu G, O'Laughlin K, Sun A, Fulla J, Kachroo N, De S. Ergonomics in the OR: An Electromyographic Evaluation of Common Muscle Groups Used During Simulated Flexible Ureteroscopy - a Pilot Study. Urology . 2022;170:66-72. doi: 10.1016/j.urology.2022.08.028. . North AC, McKenna PH, Fang R, Sener A, McNeil BK, Franc-Guimond J, Meeks WD, Schlossberg SM, Gonzalez C, Clemens JQ. Burnout in Urology: Findings from the 2016 AUA Annual Census. Urol Pract . 2018;5(6):489-494. doi: 10.1016/j.urpr.2017.11.004. Ghasemi F, Mahdavi N. A new scoring system for the Rapid Entire Body Assessment (REBA) based on fuzzy sets and Bayesian networks. International Journal of Industrial Ergonomics . 2020;80:103058. doi:10.1016/j.ergon.2020.103058 Halek RBA, Dev A, Chew KH, Hannan MA. Evaluation of Validity and Reliability of Rapid Upper Limb Assessment (RULA) Method in Research Experiment: A Systematic Review. Open Journal of Safety Science and Technology . 2025;15(1):1–13. doi:10.4236/ojsst.2025.151001 Cumpanas AD, Desai M, Landman J. MONARCH™ Robotic-Assisted Combined Mini-Percutaneous Nephrolithotomy and Flexible Ureteroscopic Lithotripsy: A Step-By-Step Guide. J Endourol . 2025;39(S1):S18-S22. doi: 10.1089/end.2024.0304. Landman J, Clayman RV, Cumpanas AD, et al. Initial Clinical Experience With a Novel Robotically Assisted Platform for Combined Mini-Percutaneous Nephrolithotomy and Flexible Ureteroscopic Lithotripsy. J Urol . 2024;212(3):483-493. doi: 10.1097/JU.0000000000004079. Desai MM, Grover R, Aron M, Ganpule A, Joshi SS, Desai MR, Gill IS. Robotic flexible ureteroscopy for renal calculi: initial clinical experience. J Urol. 2011 Aug;186(2):563-8. doi: 10.1016/j.juro.2011.03.128. Saglam R, Muslumanoglu AY, Tokatlı Z, et al. A new robot for flexible ureteroscopy: development and early clinical results (IDEAL stage 1-2b). Eur Urol . 2014;66(6):1092-100. doi: 10.1016/j.eururo.2014.06.047. El-Hajj A, Abou Chawareb E, Zein M, Wahoud N. First prospective clinical assessment of the ILY ® robotic flexible ureteroscopy platform. World J Urol . 2024 Mar 13;42(1):143. doi: 10.1007/s00345-024-04869-7. Kim J, Park H, Kwon DS, Lee JY, Cho SY. Robotic flexible ureteroscopy system, Zamenix R, demonstrates efficacy and safety in initial clinical evaluation for retrograde intrarenal surgery. Sci Rep . 2025;19;15(1):17366. doi: 10.1038/s41598-025-94031-z. Gopi P, Ishfaq M, Shkoukani ZW, et al. Robotic Flexible Ureteroscopy: Systematic Review and Meta-Analysis of Surgical Efficacy, Safety and Ergonomic Outcomes. Cureus . 2025;18;17(8):e90447. doi: 10.7759/cureus.90447. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323–1341. doi:10.1016/j.mri.2012.05.001. Kim A, Hak AJ, Choi WS, Paick SH, Kim HG, Park H. Comparison of Long-term Effect and Complications Between Holmium Laser Enucleation and Transurethral Resection of Prostate: Nations-Wide Health Insurance Study. Urology. 2021 Aug;154:300-307. doi: 10.1016/j.urology.2021.04.019. Wang C, Liang H, Chen H et al. Clinical validation of an AI-assisted system for real-time kidney stone detection during flexible ureteroscopic surgery. npj Digit. Med. 2025; 8.1: 728. https://doi.org/10.1038/s41746-025-02109-9 Wong AY, Kociolek AM, Keir PJ. The effects of altered blood flow, force, wrist posture, finger movement speed, and population on motion and blood flow in the carpal tunnel: a mega-analysis. Biomechanics. (2673-7078) , 2025, 5.1. Cutts S, Gangoo S, Modi N, Pasapula C. Tennis elbow: a clinical review article. J Orthop. 2020;17:203–207. doi: 10.1016/j.jor.2019.08.005. Razavi S, Udedibia E, Chrouser KL, et al. Urologist's Fatigue and Discomfort in Different Body Regions After Performing Flexible Ureteroscopy. Urology . 2025;200:238-244. doi: 10.1016/j.urology.2025.04.004. Landman J, Clayman R.V, Cumpanas A.D, et al. Initial clinical experience with a novel robotically assisted platform for combined mini-percutaneous nephrolithotomy and flexible ureteroscopic lithotripsy. J Urol. 2024;212(3):483–493. doi:10.1097/JU.0000000000004079. Ebina K, Abe T, Hotta K, et al. External validation of a motion capture–based surgical skill assessment system in laparoscopic simulation training environments. Surg Endosc. 2025;39:5879–5888. doi:10.1007/s00464-025-12018-3. Ishida T, Ino T, Yamakawa Y, Wada N, Koshino Y, Samukawa M, Kasahara S, Tohyama H. Estimation of vertical ground reaction force during single-leg landing using two-dimensional video images and pose estimation artificial intelligence. Phys Ther Res. 2024;27(1):35–41. doi:10.1298/ptr.E10276.. Puttmann K, Posid T, Rose J, Lee C, Bellows F. Assessment of a Novel Urology Resident Simulation-Based Curriculum. Urol Pract . 2021;8(3):402-408. doi: 10.1097/UPJ.0000000000000223. Michaud F, Márquez G, Giraldez-García MA, et al. Comparison of subject-specific musculoskeletal model calibration strategies on muscle force and fatigue estimation. J Neuroeng Rehabil. 2025;22:156. doi:10.1186/s12984-025-01691-z. Gabrielson AT, Clifton MM, Pavlovich CP, et al. Surgical ergonomics for urologists: a practical guide. Nat Rev Urol. 2021;18(3):160–169. doi:10.1038/s41585-020-00414-4. Ciccarelli M, Papetti A, Germani M. Empowering industry 5.0: automated sensor-based ergonomic risk assessment. Int J Interact Des Manuf. 2025;19:7731–7753. doi:10.1007/s12008-025-02412-5. Nygaard NPB, Thomsen GF, Rasmussen J, et al. Ergonomic and individual risk factors for musculoskeletal pain in the ageing workforce. BMC Public Health. 2022;22:1975. doi:10.1186/s12889-022-14386-0. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Mar, 2026 Reviews received at journal 22 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviews received at journal 17 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 29 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8729503","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588699329,"identity":"5d52386e-7fdb-47b6-a3b6-3dcb6b757d29","order_by":0,"name":"Satoshi Kobayashi","email":"data:image/png;base64,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","orcid":"","institution":"Kyushu University","correspondingAuthor":true,"prefix":"","firstName":"Satoshi","middleName":"","lastName":"Kobayashi","suffix":""},{"id":588699330,"identity":"34594b74-9514-4072-a4ed-0966dd6b7847","order_by":1,"name":"Keiji Tsukino","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Keiji","middleName":"","lastName":"Tsukino","suffix":""},{"id":588699331,"identity":"1ee8c573-4bbb-4355-87e7-73a3896b86c8","order_by":2,"name":"Mikifumi Koura","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Mikifumi","middleName":"","lastName":"Koura","suffix":""},{"id":588699332,"identity":"fd7926a9-3cdf-431d-b69f-b3daa38b45ea","order_by":3,"name":"Tokiyoshi Tanegashima","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Tokiyoshi","middleName":"","lastName":"Tanegashima","suffix":""},{"id":588699334,"identity":"f3d4af57-b0a3-4967-84ed-3f2a02437b6c","order_by":4,"name":"Shigehiro Tsukahara","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Shigehiro","middleName":"","lastName":"Tsukahara","suffix":""},{"id":588699335,"identity":"bf2dc798-1685-48b1-b7d4-3b8a80576a6d","order_by":5,"name":"Takashi Matsumoto","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Matsumoto","suffix":""},{"id":588699336,"identity":"a722dc68-3114-4d1f-8866-232f93ff62cb","order_by":6,"name":"Masaki Shiota","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Masaki","middleName":"","lastName":"Shiota","suffix":""},{"id":588699337,"identity":"17aeeb7e-88a1-4118-830f-efa40952c661","order_by":7,"name":"Masatoshi Eto","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Masatoshi","middleName":"","lastName":"Eto","suffix":""}],"badges":[],"createdAt":"2026-01-29 09:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8729503/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8729503/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102874789,"identity":"20ebc6fd-5e3f-4930-a532-34aa01dcebdc","added_by":"auto","created_at":"2026-02-17 19:21:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":855094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of experimental procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. Using computed tomography urogram images, the renal pelvis, renal calyces, upper ureter, rib, and spine were segmented. These segmentation data were then converted into STL files for 3D modeling, and the resulting models were fabricated with a 3D printer.\u003c/p\u003e\n\u003cp\u003eb. A uroscope-specific endoscopic video image processor and a single-use ureteroscope were employed to analyze the urologist's posture using AI-based pose estimation.\u003c/p\u003e\n\u003cp\u003ec. The kidney model was assembled with the rib and spine models, while the upper ureter was connected to a 12/14 Fr ureteral access sheath.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAI\u003c/em\u003e: artificial intelligence; \u003cem\u003eFr\u003c/em\u003e: French; \u003cem\u003eSTL\u003c/em\u003e: standard triangulated language; \u003cem\u003e3D\u003c/em\u003e: three-dimensional.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8729503/v1/3798ef533f351dc55c24ca81.png"},{"id":103049253,"identity":"60e16764-7174-4770-bad7-2562d9ba447a","added_by":"auto","created_at":"2026-02-20 07:39:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":832109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental procedures utilizing an AI-based pose estimation architecture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. First, urologists initiated the experiment by holding the fURS device in their right hand, with the right elbow and shoulder flexed. To facilitate posture analysis, a video was recorded from a frontal position.\u003c/p\u003e\n\u003cp\u003eb. The left image presents the results of automatic landmark identification in each video frame using the assembled AI-based pose estimation model. The right image provides an overview of the identified landmark locations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAI\u003c/em\u003e: artificial intelligence; \u003cem\u003efURS\u003c/em\u003e:\u003cem\u003e \u003c/em\u003eflexible ureteroscopy.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8729503/v1/a9e9c6ae92855080d5d2fe89.png"},{"id":102874790,"identity":"47ae2a57-212e-48b8-9947-f4f87abb3159","added_by":"auto","created_at":"2026-02-17 19:21:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":418462,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of AI-based pose estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. For each frame, the distance traveled by all landmarks during the fURS procedure was recorded. These data were used to calculate cumulative landmark movement distance and movement speed.\u003c/p\u003e\n\u003cp\u003eb. The area of the polygon formed by the time-series trajectory of each joint from the initial to the final frame was calculated as the movement area.\u003c/p\u003e\n\u003cp\u003ec. Additionally, the area of the smallest convex polygon encompassing the entire range of joint movement was defined as the movement range.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAI\u003c/em\u003e: artificial intelligence; \u003cem\u003efURS\u003c/em\u003e:\u003cem\u003e \u003c/em\u003eflexible ureteroscope.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8729503/v1/24a883bbfbf2ecf94edb63dc.png"},{"id":103056530,"identity":"cfddf0e0-76d8-430f-8f8b-b6ca9565d3c1","added_by":"auto","created_at":"2026-02-20 09:13:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of distance and speed for experts and novices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe vertical axis displays the results for landmarks on the left side of the trunk, along with the corresponding p-values from the comparison between the two groups. The horizontal axis displays the results for the right side of the trunk and the corresponding p-value.\u003cbr\u003e\na-c.\u0026nbsp;\u0026nbsp; Experts demonstrated significantly shorter movement distances at the left shoulder, left elbow, left wrist, and right elbow.\u003c/p\u003e\n\u003cp\u003ed-f\u0026nbsp;\u0026nbsp;\u0026nbsp; The movement speed of the left shoulder was significantly faster among experts.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eStatistically significant (p\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8729503/v1/cc0856c6807a7295525ecd7f.png"},{"id":102963308,"identity":"15ab33ff-2513-47d1-bfdd-241d717658bb","added_by":"auto","created_at":"2026-02-19 04:15:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":205064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of area and range for experts and novices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe vertical axis displays the results for landmarks on the left side of the trunk, along with the corresponding p-values from the comparison between the two groups. The horizontal axis displays the results for the right side of the trunk and the corresponding p-value.\u003c/p\u003e\n\u003cp\u003ea-c.\u0026nbsp;\u0026nbsp; In the expert group, the area of the left shoulder joint and the area of the right elbow joint were significantly narrower.\u003c/p\u003e\n\u003cp\u003ed-f\u0026nbsp;\u0026nbsp;\u0026nbsp; In the expert group, the movement range of both the shoulder and elbow joints was significantly narrower, while only the left wrist joint showed a significantly narrower range.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eStatistically significant (p\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8729503/v1/f0ccae766632bc7fea8734e2.png"},{"id":102963837,"identity":"201a75b0-8666-4b69-af92-f64e9fe0ef11","added_by":"auto","created_at":"2026-02-19 04:20:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":102972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of angle fluctuation range for experts and novices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe vertical axis displays the results for landmarks on the left side of the trunk, along with the corresponding p-values from the comparison between the two groups. The horizontal axis displays the results for the right side of the trunk and the corresponding p-value from the analysis comparing the two groups.\u003c/p\u003e\n\u003cp\u003ea. Angles of the shoulder were calculated using three anatomical points: the elbow, hip, and shoulder, with the shoulder joint as the central reference. Experts demonstrated a significantly narrower range of angle fluctuations at the right shoulder joint.\u003c/p\u003e\n\u003cp\u003eb. Angles of the elbow were calculated using three anatomical points: the wrist, shoulder, and elbow, with the elbow joint as the central reference. Experts demonstrated a significantly narrower range of angle fluctuations at the left elbow joint.\u003c/p\u003e\n\u003cp\u003e*Statistically significant (p\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8729503/v1/1c70169a0c26e44fda73c86d.png"},{"id":103056978,"identity":"efa41456-fbb7-422a-bf9a-99d43888a500","added_by":"auto","created_at":"2026-02-20 09:27:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3688211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8729503/v1/f226b4f6-239b-4dd9-87bf-7e7ec8c27bf9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantifying ergonomic challenges for urologists operating flexible ureteroscopes through artificial intelligence-based posture estimation toward a paradigm shift to robot-assisted ureteroscopy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe lifetime prevalence of urinary stones is approximately 1 in 7 men and 1 in 15 women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite preventive strategies, recurrence rates remain high, reaching 45% at 5 years and 60% at 10 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Transurethral ureteroscopy (TUL) for urinary stones, utilizing a flexible ureteroscopy (fURS) device, has achieved high stone-free rates and an acceptable complication profile. However, recurrence rates can be up to 2.25 times higher depending on stone location [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As the incidence and complexity of stone disease increase due to increased obesity and metabolic complications, the number of TUL procedures being carried out continues to rise at a rate of 6\u0026ndash;7% per year, placing greater demands on endourologists and operating-room resources [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Increased surgeon workload associated with TUL has been suggested as a long-term cause of carpal tunnel syndrome and elbow arthritis attributed to repetitive wrist and arm movements in a static posture. These symptoms have been linked to burnout and reduced productivity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, no studies have examined the relationship between posture during ureteroscopy and functional impairment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although the Rapid Entire Body Assessment and Rapid Upper Limb Assessment scoring systems are considered optimal tools for evaluating ergonomics during ureteroscopy, these indices rely on observational assessment and are limited in the ergonomic evaluation of fURS by their subjective nature and the use of time sampling [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRobot-assisted ureteroscopy (rURS) is an emerging technology in stone treatment that potentially heralds a paradigm shift away from fURS. Its feasibility is currently under evaluation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. rURS has been introduced as an alternative to percutaneous nephrolithotripsy due to its potential to provide improved operability and enhanced access to each renal calyx, as compared to fURS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Several studies have demonstrated that rURS reduces physical fatigue and musculoskeletal strain among endourologists, relative to fURS [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This is hypothesized to include the significant alleviation of repetitive wrist movements and awkward arm positioning [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, few existing studies have analyzed endourologists\u0026rsquo; posture, making it challenging objectively to identify unmet ergonomic needs associated with traditional fURS. Consequently, the ergonomic benefits of rURS for endourologists remain to be comprehensively evaluated.\u003c/p\u003e \u003cp\u003eIn response to these gaps, this study developed an artificial intelligence (AI)-based pose estimation pipeline specifically for TUL to objectively identify ergonomic issues associated with fURS. These issues were then evaluated by analyzing endourologists\u0026rsquo; posture during simulated TUL procedures.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatement of Ethics\u003c/h2\u003e \u003cp\u003e All procedures in this study were performed in accordance with the 1964 Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board (approval no. 28062) of Kyushu University. Moreover, informed consent was obtained from all the participants, and they were provided with clear opportunities to opt out, respecting their autonomy and right to choose.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eKidney and Bone Phantom Models\u003c/h3\u003e\n\u003cp\u003eA segmentation tool (3D Slicer version 4.11, Brigham and Women\u0026rsquo;s Hospital, Boston, MA, USA) was used to annotate the spine, ribs, renal pelvis, calyces, and upper ureter for each computed tomography scan slice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-a). Artificial supports for the kidney model were designed within the annotation data and placed on the spine\u0026ndash;rib model [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The annotations were converted into a Standard Triangulated Language file. The bone model was fabricated using three-dimensional (3D) printing with acrylonitrile-butadiene-styrene resin. The kidney model was produced by casting a kidney mold and injecting silicone resin. A stone resembling a urinary stone was placed in each renal calyx within the kidney model. A uroscope-specific endoscopic video image processor (DVM Model B, Tokyo Kikai Boeki Corporation, Japan) and a single-use ureteroscope (Uroscope, Tokyo Kikai Boeki Corporation, Japan) were utilized. The ureteroscope featured an 8.4 Fr diameter and 285\u0026deg; upper and lower curvature angles. A 300W xenon short-arc lamp with a color temperature of 5800 K served as the fURS light source (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-b). Kidney and bone phantom models were arranged and a 12/14 Fr ureteral access sheath (Coloplast, Denmark) was inserted into the upper ureter of the kidney model to simulate the fURS procedure in real TUL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eParticipant Eligibility\u003c/h3\u003e\n\u003cp\u003eExpert participants were defined as those who hold board-certified specialists qualifications by the Japanese Urological Association. Novice participants were residents with less than 1 year of experience as urologists and without specialist qualifications. All participants provided informed consent and were briefed on the validation task and evaluation method prior to the experiment.\u003c/p\u003e\n\u003ch3\u003eExperiment Procedures\u003c/h3\u003e\n\u003cp\u003eEach subject faced the phantom model in the default posture prior to the task (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-a), operated the fURS device to reach the renal pelvis of the phantom model and accessed the upper, middle, and lower renal calyces sequentially, starting from the top. Targets placed within each calyx were visually observed in the center of the endoscopic image.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDataset Preparation\u003c/h3\u003e\n\u003cp\u003eTo measure the positions of the subjects\u0026rsquo; shoulder, elbow, and wrist joints, their movements during the fURS procedure were video-recorded at 30 frames per second. A webcam (BSW300MBK, BUFFALO, 1920 \u0026times; 1080 px) captured the video throughout the experimental procedure.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAI-Based Pose Estimation Architecture\u003c/h2\u003e \u003cp\u003eThe MediaPipe Pose Landmark model was used to obtain two-dimensional (2D) estimates of the subjects\u0026rsquo; joint coordinates for each video frame. The coordinates of 22 out of 33 full-body landmarks were analyzed and the estimated landmarks were superimposed on the video (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-b). For each frame, the landmark coordinates of the shoulders, elbows, and wrists ([shoulder_x, shoulder_y], [elbow_x, elbow_y], [wrist_x, wrist_y]) were converted to a Comma-Separated Values file for subsequent metric analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis Metrics\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAccumulated travel distance (ATD) of each landmark\u003c/h2\u003e \u003cp\u003eThe distance each landmark moved in each image frame was measured, and the cumulative distance was calculated as ADT in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-a.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMovement speed (MS) of each landmark\u003c/h2\u003e \u003cp\u003eThe MS of each anatomical landmark (joint) was calculated for each frame.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMovement area (MA) of each landmark (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-b)\u003c/h2\u003e \u003cp\u003eFrom the 2D video images, the range of motion for anatomical landmarks (joints) was calculated as the MA. Assuming that the points (coordinates) that make up the trajectory\u0026mdash;meaning the sequential path traced by the anatomical landmark (joint) positions\u0026mdash;the area of the trajectory when considered as a closed figure was calculated using the Shoelace formula.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMovement range (MR) of each landmark (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-c)\u003c/h2\u003e \u003cp\u003eThe area of the smallest convex polygon connecting the outermost points to which the landmark (joint) moved was calculated as the MR using the convex hull from 2D video images.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAngle fluctuation range (AFR) of each landmark\u003c/h2\u003e \u003cp\u003eThe angle of the vector from one joint to the other two points was calculated using the arctangent function. The coordinates of the shoulder, elbow, and wrist were utilized, and the difference between the two angles was expressed in degrees.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSubjects were categorized into expert and novice groups and the fURS device operation was assessed respectively. The ATD, MS, MA, MR, and AFR were reported as medians with interquartile ranges. Data with a normal distribution and a p-value of 0.05 or greater in the Shapiro\u0026ndash;Wilk test were analyzed using t-tests. Data not following a normal distribution and with a p-value of less than 0.05 were analyzed using the Wilcoxon test. All analyses were conducted using JMP Pro version 19.0.0 for Mac (SAS Institute Inc., Cary, NC, USA). Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All p-values were evaluated using two-side tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBaseline urologist\u0026rsquo;s characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the baseline characteristics of the urologists who participated in this study. All subjects were right-handed and operated the fURS device with their right hand. Experts had a significantly higher number of performing fURS in lifetime (Expert group vs. Novice group: 100 vs. 10 cases, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline urologists\u0026rsquo; characteristics\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian, (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpert group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNovice group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (36\u0026ndash;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (28\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eMale, n [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 [100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 [100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight handed, n [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 [100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 [100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of experience as a doctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (9\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eNumber of Board Certified Specialists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eSupervising physician qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of fURS performed in lifetime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (100\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003e\u0026lt;\u0026thinsp;50 cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;100 cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100 cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003efURS\u003c/em\u003e: flexible ureteroscopy; \u003cem\u003eIQR\u003c/em\u003e: Interquartile Range\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePerforming fURS\u003c/h2\u003e \u003cp\u003eAll participants were able to complete the task. Experts required significantly less time to complete observation of the entire renal pelvis using the fURS device compared to novices (median: 58 vs. 102 seconds, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMetrics by AI-based pose estimation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn experts, the left shoulder joint exhibited a smaller MA (6.29 vs. 40.5 cm\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and range (23.4 vs. 148 cm\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the right shoulder also demonstrated a reduced MR (50.7 vs. 318 cm\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.040) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e-a, d). On the other hand, there was no significant difference in the MA of the right shoulder joint (16.2 vs. 41.6 cm\u003csup\u003e2\u003c/sup\u003e, p\u0026thinsp;=\u0026thinsp;0.242). The right elbow joint showed both a smaller MA (17.3 vs. 281 cm\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.040) and MR (91.6 vs. 1112 cm\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.019). The left elbow joint also had a reduced MR (780 vs. 187 cm\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.013) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e-b, e). There was no significant difference in MA values at the left elbow joint (16.0 vs. 32.0 cm\u003csup\u003e2\u003c/sup\u003e, p\u0026thinsp;=\u0026thinsp;0.661). The left wrist\u0026rsquo;s MA was smaller in experts (303 vs. 1188 cm\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.019) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e-c, f). There were no significant differences in the MA in either wrist joint (135 vs. 32.5 cm\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.558; 48.7 vs. 94.6 cm\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.188) or in the MR in the right wrist joint (540 vs. 1025 cm\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.107).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study developed and applied an AI-based pose-estimation pipeline to objectively evaluate ergonomic challenges encountered by surgeons using a fURS during TUL. Analysis of detailed kinematic parameters during simulated TUL procedures revealed clear and quantifiable differences in operative posture and movement patterns between expert and novice endourologists. Experts observed the entire renal pelvis significantly faster than novices, indicating greater procedural efficiency. More importantly, experts consistently exhibited reduced ATD, smaller MA, and narrower MR across multiple upper-body joints, particularly in the shoulders, elbows, and wrists.\u003c/p\u003e \u003cp\u003eOur findings suggest that expert surgeons adopt a stable movement strategy, characterized by rotating the fURS device primarily around the elbow while minimizing unnecessary shoulder and wrist excursions. This approach resulted in shorter ATD for both elbows and the left wrist, as well as significantly restricted MA and angular fluctuation ranges in the shoulder and elbow joints. In contrast, novices demonstrated excessive and dispersed joint movements, likely reflecting compensatory postures and inefficient motor control during fURS manipulation. These differences were not consistently captured in the MS alone, underscoring the importance of comprehensive kinematic analysis as opposed to reliance on isolated metrics.\u003c/p\u003e \u003cp\u003eThis study provides objective evidence linking surgeon expertise to ergonomic efficiency, which addresses a critical gap in the literature, previous studies having relied largely on subjective observational tools such as the Rapid Entire Body Assessment and Rapid Upper Limb Assessment scoring systems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. By visualizing and quantifying posture-related functional impairment, our AI-based framework enables precise identification of ergonomic stressors inherent to fURS [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These results not only enhance understanding of skill-dependent ergonomic adaptation but also provide a foundation for targeted training strategies and the objective evaluation of the ergonomic advantages of emerging technologies such as rURS [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study finds that right-handed expert urologists performed fURS efficiently; however, although the ATD, MA, and MR of the right wrist did not change, rotational movement, dorsiflexion, and extension of the right wrist likely increased. As increased wrist rotation or extension is directly associated with carpal tunnel syndrome, these findings indicate that technical proficiency does not eliminate the long-term ergonomic risks associated with fURS [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For the non-dominant hand, a reduction in elbow flexion angle during fURS resulted in increased forearm flexion, thereby elevating the risk of elbow arthritis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Consequently, the risk of elbow arthritis remains for long-term fURS procedures [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. rURS is performed using a gamepad and does not require the upper-limb movements characteristic of fURS, potentially reducing ergonomic risks [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, this study objectively demonstrates that fURS poses risks of carpal tunnel syndrome and elbow arthritis among urologists, while also identifying rURS as a feasible ergonomic and clinical alternative [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe rURS has emerged as an advanced modality for urinary stone management, with the goal of enhancing clinical outcomes. The initial prospective clinical evaluation of the ILY\u0026reg; rURS platform in the management of kidney stones, conducted in 29 patients with a cumulative total of 45 stones, demonstrated both safety and efficacy, the platform achieving a stone-free rate of 93.6% and only minimal Clavien\u0026ndash;Dindo grade I complications [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A meta-analysis of studies evaluating the ILY\u0026reg; rURS manipulator platform and the MONARCH\u0026trade; platform reported stone-free rates of approximately 86.0\u0026ndash;87.4%, which are comparable to those achieved with traditional fURS, as well as ergonomic advantages such as reduced surgeon fatigue and lower radiation exposure [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Nevertheless, although rURS offers ergonomic benefits, the underlying ergonomic challenges of fURS\u0026mdash;such as awkward hand positioning, extended periods of gripping, and strain from prolonged procedures\u0026mdash;have not been clearly delineated in the literature.\u003c/p\u003e \u003cp\u003eThe present study contributes to the expanding body of evidence supporting the ergonomic advantages of rURS. However, several limitations should be acknowledged. First, the analysis was performed in a simulated TUL environment rather than during live clinical procedures. Simulation provides standardized conditions and precise motion capture [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, it fails fully to replicate the cognitive demands, time constraints, and anatomical variability of real surgical settings; thus, surgeons\u0026rsquo; observed posture and movement patterns may not accurately reflect actual patient care [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Previous studies have demonstrated high accuracy in tracking upper-limb trajectories using MediaPipe. Building on these findings, it is necessary to conduct a comparative analysis during TUL to determine how MediaPipe's tracking accuracy compares with optical motion capture using optical markers, which is considered the gold standard [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Second, the small size of the sample, which was taken from a single institution, may restrict generalizability. A large volume of time-series data frames was collected from continuous surgical movements, thereby enhancing statistical reliability [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Third, the study focuses only on upper-body kinematics via 2D pose estimation. Although the AI-based pipeline enables objective assessment of joint movement, it does not evaluate muscle activation, force exertion, or static muscle loading, all of which contribute to musculoskeletal fatigue and injury.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] However, increased ATD indicates repetitive stress on joints and muscles, a known risk factor in the development of carpal tunnel syndrome. Similarly, understanding the causal link between AFR and anatomical stress in elbow arthritis requires measuring AFR's effects on joint dynamics. Employing wearable sensors, electromyography, and force-measuring devices to monitor wrist joint angle, muscle activation, and force production directly connect observed ergonomic risks to musculoskeletal fatigue and injury [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Future studies should use wearable sensors, electromyography, and force-measurement devices to monitor wrist joint angles, muscle activation, and force exertion, enabling a comprehensive assessment of ergonomic risks contributing to musculoskeletal fatigue and injury [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Fourth, the ergonomic evaluation relates only to fURS procedures, with no comparisons to robot-assisted or other endourological modalities [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The ergonomic advantages of alternative platforms cannot therefore be assessed. Finally, the cross-sectional design prevents causal inferences regarding observed movement patterns and long-term musculoskeletal disorders [31]. Future research should include longitudinal studies that integrate clinical outcomes, surgeon-reported symptoms, and intraoperative biomechanical data to clarify whether identified ergonomic differences reduce fatigue and injury risk.\u003c/p\u003e \u003cp\u003eDespite these limitations, the study has several strengths. It objectively demonstrates the ergonomic challenges of fURS through an AI-based pose estimation pipeline. The findings suggest that transitioning to rURS is feasible from an ergonomic perspective.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has identified ergonomic challenges associated with operating fURS devices using a specialized AI-based pose-estimation pipeline. Ergonomic performance was assessed objectively using the pipeline to analyze surgical hand and body positioning. The results demonstrate that rURS is ergonomically feasible for treating the upper urinary tract. Additionally, the AI-based framework provides objective feedback on ergonomic techniques.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAFR: Angle fluctuation range\u003c/p\u003e\n\u003cp\u003eATD: Accumulated travel distance\u003c/p\u003e\n\u003cp\u003efURS: flexible ureteroscopy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMA: Movement area\u003c/p\u003e\n\u003cp\u003eMR: Movement range\u003c/p\u003e\n\u003cp\u003eMS: movement speed\u003c/p\u003e\n\u003cp\u003erURS: robot-assisted ureteroscopy\u003c/p\u003e\n\u003cp\u003eTUL: Transurethral ureteroscopy\u003c/p\u003e\n\u003cp\u003e2D: two-dimensional\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3D: three-dimensional\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by JSPS KAKENHI Grant Numbers JP23K19219 and 25K15937, Japanese Foundation for Research and Promotion of Endoscopy Grant, and Japanese Society of Endourology and Robotics Robot-assisted Surgery Research Grant A (2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Involving Human Participants and / or Animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support this study\u0026rsquo;s findings are available from the corresponding author [Satoshi Kobayashi] upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of potential conflicts of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors hereby state that they have no relevant financial interests to declare.\u003cbr\u003e\u003cstrong\u003eResearch involving human participants and / or animals:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all the participants, and they were provided with clear opportunities to opt out, respecting their autonomy and right to choose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by JSPS KAKENHI Grant Numbers JP23K19219 and 25K15937, Japanese Foundation for Research and Promotion of Endoscopy Grant (2025), and Japanese Society of Endourology and Robotics Robot-assisted Surgery Research Grant A (2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to writing, review, and editing. Satoshi Kobayashi was involved in the design and implementation of the study, model creation, data collection and interpretation, and drafting of the paper. Keiji Tsukino provided technical support for the AI-based pose estimation and analyzed some of the data. Mikifumi Koura contributed technical support and data collection. Masaki Shiota and Masatoshi Eto provided guidance and supervision, respectively, and ensured the smooth progress of the research. This paper is the culmination of our joint efforts and all authors have read and approved the final version.\u003c/p\u003e\n\u003cp\u003eSatoshi Kobayashi: Protocol/project development, Data collection or management, Data analysis, Manuscript writing/editing\u003c/p\u003e\n\u003cp\u003eKeiji Tsukino: project development, Data collection, Data analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMikifumi Koura: Data collection, Manuscript writing/editing\u003c/p\u003e\n\u003cp\u003eTokiyoshi Tanegashi: Manuscript writing/editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShigehiro Tsukahara: Manuscript writing/editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTakashi Matsumoto: Writing - review \u0026amp; editing, Writing - original draft\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMasaki Shiota: : Writing - review \u0026amp; editing, Writing - original draft\u003c/p\u003e\n\u003cp\u003eMasatoshi Eto: Supervision, Writing - : Writing - review \u0026amp; editing, Writing - original draft, management\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu Y, Chen Y, Liao B, Luo D, Wang K, Li H, Zeng G. Epidemiology of urolithiasis in Asia. \u003cem\u003eAsian J Urol\u003c/em\u003e. 2018;5(4):205\u0026ndash;214. doi: 10.1016/j.ajur.2018.08.007.\u003c/li\u003e\n\u003cli\u003eQian X, Wan J, Xu J, Liu C, Zhong M, Zhang J, Zhang Y, Wang S. Epidemiological Trends of Urolithiasis at the Global, Regional, and National Levels: A Population-Based Study. \u003cem\u003eInt J Clin Pract\u003c/em\u003e. 2022;2022:6807203. doi: 10.1155/2022/6807203. \u003c/li\u003e\n\u003cli\u003eKhan SR, Pearle MS, Robertson WG, Gambaro G, Canales BK, Doizi S, Traxer O, Tiselius HG. Kidney stones. \u003cem\u003eNat Rev Dis Primers.\u003c/em\u003e 2016;2:1-23. https://doi.org/doi:10.1038/nrdp.2016.8\u003c/li\u003e\n\u003cli\u003eBhanot R, Jones P, Somani B. Minimally Invasive Surgery for the Treatment of Ureteric Stones - State-of-the-Art Review. \u003cem\u003eRes Rep Urol.\u003c/em\u003e 2021:6;13:227-236. doi: 10.2147/RRU.S311010. \u003c/li\u003e\n\u003cli\u003eGabrielson AT, Clifton MM, Pavlovich CP, Biles MJ, Huang M, Agnew J, Pierorazio PM, Matlaga BR, Bajic P, Schwen ZR. Surgical ergonomics for urologists: a practical guide. \u003cem\u003eNat Rev Urol\u003c/em\u003e. 2021:18(3):160-169. doi: 10.1038/s41585-020-00414-4.\u003c/li\u003e\n\u003cli\u003eWright HC, Gheordunescu G, O\u0026apos;Laughlin K, Sun A, Fulla J, Kachroo N, De S. Ergonomics in the OR: An Electromyographic Evaluation of Common Muscle Groups Used During Simulated Flexible Ureteroscopy - a Pilot Study. \u003cem\u003eUrology\u003c/em\u003e. 2022;170:66-72. doi: 10.1016/j.urology.2022.08.028. .\u003c/li\u003e\n\u003cli\u003eNorth AC, McKenna PH, Fang R, Sener A, McNeil BK, Franc-Guimond J, Meeks WD, Schlossberg SM, Gonzalez C, Clemens JQ. Burnout in Urology: Findings from the 2016 AUA Annual Census. \u003cem\u003eUrol Pract\u003c/em\u003e. 2018;5(6):489-494. doi: 10.1016/j.urpr.2017.11.004.\u003c/li\u003e\n\u003cli\u003eGhasemi F, Mahdavi N. A new scoring system for the Rapid Entire Body Assessment (REBA) based on fuzzy sets and Bayesian networks. \u003cem\u003eInternational Journal of Industrial Ergonomics\u003c/em\u003e. 2020;80:103058. doi:10.1016/j.ergon.2020.103058\u003c/li\u003e\n\u003cli\u003eHalek RBA, Dev A, Chew KH, Hannan MA. Evaluation of Validity and Reliability of Rapid Upper Limb Assessment (RULA) Method in Research Experiment: A Systematic Review. Open \u003cem\u003eJournal of Safety Science and Technology\u003c/em\u003e. 2025;15(1):1\u0026ndash;13. doi:10.4236/ojsst.2025.151001\u003c/li\u003e\n\u003cli\u003eCumpanas AD, Desai M, Landman J. MONARCH\u0026trade; Robotic-Assisted Combined Mini-Percutaneous Nephrolithotomy and Flexible Ureteroscopic Lithotripsy: A Step-By-Step Guide. \u003cem\u003eJ Endourol\u003c/em\u003e. 2025;39(S1):S18-S22. doi: 10.1089/end.2024.0304. \u003c/li\u003e\n\u003cli\u003eLandman J, Clayman RV, Cumpanas AD, et al. Initial Clinical Experience With a Novel Robotically Assisted Platform for Combined Mini-Percutaneous Nephrolithotomy and Flexible Ureteroscopic Lithotripsy.\u003cem\u003e J Urol\u003c/em\u003e. 2024;212(3):483-493. doi: 10.1097/JU.0000000000004079. \u003c/li\u003e\n\u003cli\u003eDesai MM, Grover R, Aron M, Ganpule A, Joshi SS, Desai MR, Gill IS. Robotic flexible ureteroscopy for renal calculi: initial clinical experience. \u003cem\u003eJ Urol.\u003c/em\u003e 2011 Aug;186(2):563-8. doi: 10.1016/j.juro.2011.03.128. \u003c/li\u003e\n\u003cli\u003eSaglam R, Muslumanoglu AY, Tokatlı Z, et al. A new robot for flexible ureteroscopy: development and early clinical results (IDEAL stage 1-2b). \u003cem\u003eEur Urol\u003c/em\u003e. 2014;66(6):1092-100. doi: 10.1016/j.eururo.2014.06.047. \u003c/li\u003e\n\u003cli\u003eEl-Hajj A, Abou Chawareb E, Zein M, Wahoud N. First prospective clinical assessment of the ILY\u003csup\u003e\u0026reg;\u003c/sup\u003e robotic flexible ureteroscopy platform. \u003cem\u003eWorld J Urol\u003c/em\u003e. 2024 Mar 13;42(1):143. doi: 10.1007/s00345-024-04869-7. \u003c/li\u003e\n\u003cli\u003eKim J, Park H, Kwon DS, Lee JY, Cho SY. Robotic flexible ureteroscopy system, Zamenix R, demonstrates efficacy and safety in initial clinical evaluation for retrograde intrarenal surgery. \u003cem\u003eSci Rep\u003c/em\u003e. 2025;19;15(1):17366. doi: 10.1038/s41598-025-94031-z. \u003c/li\u003e\n\u003cli\u003eGopi P, Ishfaq M, Shkoukani ZW, et al. Robotic Flexible Ureteroscopy: Systematic Review and Meta-Analysis of Surgical Efficacy, Safety and Ergonomic Outcomes. \u003cem\u003eCureus\u003c/em\u003e. 2025;18;17(8):e90447. doi: 10.7759/cureus.90447.\u003c/li\u003e\n\u003cli\u003eFedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. \u003cem\u003eMagn Reson Imaging.\u003c/em\u003e 2012;30(9):1323\u0026ndash;1341. doi:10.1016/j.mri.2012.05.001.\u003c/li\u003e\n\u003cli\u003eKim A, Hak AJ, Choi WS, Paick SH, Kim HG, Park H. Comparison of Long-term Effect and Complications Between Holmium Laser Enucleation and Transurethral Resection of Prostate: Nations-Wide Health Insurance Study. \u003cem\u003eUrology.\u003c/em\u003e 2021 Aug;154:300-307. doi: 10.1016/j.urology.2021.04.019.\u003c/li\u003e\n\u003cli\u003eWang C, Liang H, Chen H \u003cem\u003eet al.\u003c/em\u003e Clinical validation of an AI-assisted system for real-time kidney stone detection during flexible ureteroscopic surgery. \u003cem\u003enpj Digit. Med.\u003c/em\u003e 2025; 8.1: 728. https://doi.org/10.1038/s41746-025-02109-9 \u003c/li\u003e\n\u003cli\u003eWong AY, Kociolek AM, Keir PJ. The effects of altered blood flow, force, wrist posture, finger movement speed, and population on motion and blood flow in the carpal tunnel: a mega-analysis. \u003cem\u003eBiomechanics.\u003c/em\u003e\u003cem\u003e(2673-7078)\u003c/em\u003e, 2025, 5.1. \u003c/li\u003e\n\u003cli\u003eCutts S, Gangoo S, Modi N, Pasapula C. Tennis elbow: a clinical review article. \u003cem\u003eJ Orthop.\u003c/em\u003e 2020;17:203\u0026ndash;207. doi: 10.1016/j.jor.2019.08.005.\u003c/li\u003e\n\u003cli\u003eRazavi S, Udedibia E, Chrouser KL, et al. Urologist\u0026apos;s Fatigue and Discomfort in Different Body Regions After Performing Flexible Ureteroscopy. \u003cem\u003eUrology\u003c/em\u003e. 2025;200:238-244. doi: 10.1016/j.urology.2025.04.004. \u003c/li\u003e\n\u003cli\u003eLandman J, Clayman R.V, Cumpanas A.D, et al. Initial clinical experience with a novel robotically assisted platform for combined mini-percutaneous nephrolithotomy and flexible ureteroscopic lithotripsy. \u003cem\u003eJ Urol.\u003c/em\u003e 2024;212(3):483\u0026ndash;493. doi:10.1097/JU.0000000000004079.\u003c/li\u003e\n\u003cli\u003eEbina K, Abe T, Hotta K, et al. External validation of a motion capture\u0026ndash;based surgical skill assessment system in laparoscopic simulation training environments. \u003cem\u003eSurg Endosc.\u003c/em\u003e 2025;39:5879\u0026ndash;5888. doi:10.1007/s00464-025-12018-3.\u003c/li\u003e\n\u003cli\u003eIshida T, Ino T, Yamakawa Y, Wada N, Koshino Y, Samukawa M, Kasahara S, Tohyama H. Estimation of vertical ground reaction force during single-leg landing using two-dimensional video images and pose estimation artificial intelligence. \u003cem\u003ePhys Ther Res.\u003c/em\u003e 2024;27(1):35\u0026ndash;41. doi:10.1298/ptr.E10276..\u003c/li\u003e\n\u003cli\u003ePuttmann K, Posid T, Rose J, Lee C, Bellows F. Assessment of a Novel Urology Resident Simulation-Based Curriculum. \u003cem\u003eUrol Pract\u003c/em\u003e. 2021;8(3):402-408. doi: 10.1097/UPJ.0000000000000223.\u003c/li\u003e\n\u003cli\u003eMichaud F, M\u0026aacute;rquez G, Giraldez-Garc\u0026iacute;a MA, et al. Comparison of subject-specific musculoskeletal model calibration strategies on muscle force and fatigue estimation. \u003cem\u003eJ Neuroeng Rehabil.\u003c/em\u003e 2025;22:156. doi:10.1186/s12984-025-01691-z.\u003c/li\u003e\n\u003cli\u003eGabrielson AT, Clifton MM, Pavlovich CP, et al. Surgical ergonomics for urologists: a practical guide. \u003cem\u003eNat Rev Urol.\u003c/em\u003e 2021;18(3):160\u0026ndash;169. doi:10.1038/s41585-020-00414-4. \u003c/li\u003e\n\u003cli\u003eCiccarelli M, Papetti A, Germani M. Empowering industry 5.0: automated sensor-based ergonomic risk assessment. \u003cem\u003eInt J Interact Des Manuf.\u003c/em\u003e 2025;19:7731\u0026ndash;7753. doi:10.1007/s12008-025-02412-5.\u003c/li\u003e\n\u003cli\u003eNygaard NPB, Thomsen GF, Rasmussen J, et al. Ergonomic and individual risk factors for musculoskeletal pain in the ageing workforce. \u003cem\u003eBMC Public Health.\u003c/em\u003e 2022;22:1975. doi:10.1186/s12889-022-14386-0.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjur","sideBox":"Learn more about [World Journal of Urology](https://link.springer.com/journal/345)","snPcode":"345","submissionUrl":"https://submission.nature.com/new-submission/345/3","title":"World Journal of Urology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ureteroscopes, urinary tract, urinary calculi, artificial intelligence","lastPublishedDoi":"10.21203/rs.3.rs-8729503/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8729503/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e\u003c/em\u003e\u003cbr\u003e\nThe ergonomic challenges faced by surgeons during flexible ureteroscopy have yet to be thoroughly evaluated using objective methods. However, robot-assisted ureteroscopy has emerged as a promising treatment for urinary stones. The present study quantitatively assesses operative posture during transurethral ureteroscopy via an artificial intelligence-based posture estimation framework, and identifies kinematic differences associated with surgical expertise.\u003cbr\u003e\n\u003cem\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/em\u003e\u003cbr\u003e\nExpert and novice urologists performed standardized flexible ureteroscopic tasks in a simulated transurethral ureteroscopy environment using kidney phantoms containing artificial stones. Upper-body movements were continuously video-recorded and joint coordinates of the shoulders, elbows, and wrists were extracted using an artificial intelligence-based pose estimation system. Kinematic parameters were quantitatively analyzed and compared between groups.\u003cbr\u003e\n\u003cem\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/em\u003e\u003cbr\u003e\nExperts completed observation of the entire renal pelvis significantly faster than novices (median 58 vs. 102 seconds, p\u0026lt;0.001). Accumulated travel distance was markedly less in experts for the left shoulder (125 vs. 300 cm, p\u0026lt;0.001), right elbow (324 vs. 1035 cm, p=0.028), left elbow (349 vs. 772 cm, p=0.019), and left wrist (729 vs. 3798 cm, p\u0026lt;0.001). Experts had smaller movement areas at the left shoulder (6.29 vs. 40.5 cm\u003csup\u003e2\u003c/sup\u003e, p\u0026lt;0.001) and right elbow (17.3 vs. 281 cm\u003csup\u003e2\u003c/sup\u003e, p=0.040), and reduced movement ranges across multiple joints. Their angle fluctuation ranges were also narrower for the right shoulder (32.0 vs. 76.3 degrees, p=0.028) and left elbow (70.4 vs. 122.4 degrees, p\u0026lt;0.001).\u003cbr\u003e\n\u003cem\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/em\u003e\u003cbr\u003e\nThe artificial intelligence-based posture analysis objectively demonstrated ergonomic advantages associated with surgical expertise during flexible ureteroscopy and revealed persistent ergonomic risks to the wrist and elbow.\u003c/p\u003e","manuscriptTitle":"Quantifying ergonomic challenges for urologists operating flexible ureteroscopes through artificial intelligence-based posture estimation toward a paradigm shift to robot-assisted ureteroscopy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 19:21:28","doi":"10.21203/rs.3.rs-8729503/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-10T18:02:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T13:00:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T17:21:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T23:19:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30818957944386656372807656030536783817","date":"2026-02-17T23:08:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T16:21:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T09:56:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137881138872516396251542020471432649661","date":"2026-02-11T11:47:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25246525805452610683747481816965103489","date":"2026-02-10T04:56:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63493874815506498863333540529106057548","date":"2026-02-09T14:30:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29893431722173840762350062011307093837","date":"2026-02-09T13:51:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T09:28:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T14:16:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T17:38:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Urology","date":"2026-01-29T08:38:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjur","sideBox":"Learn more about [World Journal of Urology](https://link.springer.com/journal/345)","snPcode":"345","submissionUrl":"https://submission.nature.com/new-submission/345/3","title":"World Journal of Urology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bf6b75dc-48b5-4be3-b804-25566074e987","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T04:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 19:21:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8729503","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8729503","identity":"rs-8729503","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}