Introduction/background: Owing to restrictions in operative experiences, urology residents can no longer solely rely on 'hands-on' operative time to master their surgical skills by the end of residency. Simulation training could help residents master basic surgical skills and steps of a procedure to maximize time in the operative room. However, simulators can be expensive or tedious to set up, limiting the availability to residents and training programs.
Objective: The authors sought to develop and validate an inexpensive, high-fidelity training model for robotic pyeloplasty.
Study Design: Pyeloplasty models were created using Dragon Skin® FX-Pro tissue-mimicking silicone cast over 3-dimensional molds. Urology faculty and trainees completed a demographic questionnaire. The participants viewed a brief instructional video and then independently performed robotic dismembered pyeloplasty on the model. Acceptability and content validity were evaluated via post-task evaluation of the model. Construct validity was evaluated by comparing procedure completion time, the Global Evaluative Assessment of Robotic Skills (GEARS) score, blinded subjective physical evaluation of repair quality (1-10 scale), and flow rate between experts and novices.
Results: In total, 5 urology faculty, 6 fellows, and 14 residents participated. The median robotic console experience among faculty, fellows, and residents was 8 years (interquartile range [IQR] = 6-11), 3.5 years (IQR = 2-4 years), and 0 years (IQR = 0-0.5 years), respectively. The median procedure completion time was 29 min (IQR = 26-40 min), and the median flow rate was 1.11 mL/s (IQR = 0-1.34 mL/s). All faculty had flow rates >1.25 mL/s and procedure times <30 min compared with 2 of 6 fellows and none of the residents (P < 0.001). All faculty, half of the fellows, and none of the residents achieved a GEARS score ≥20, with a median resident score of 12.5 (IQR = 8-13) (P < 0.001). For repair quality, all faculty scored ≥9 (out of 10), all fellows scored ≥8, and the median score among residents was 6 (IQR = 2-6) (P < 0.001). The material cost was $1.32/model, and the average production time was 0.12 person-hours/model.
Discussion And Conclusion: This low-cost pyeloplasty model exhibits acceptability and content validity. Construct validity is supported by significant correlation between participant expertise and simulator performance across multiple assessment domains. The model has excellent potential to be used as a training tool in urology and allows for repetitive practice of pyeloplasty skills before live cases.
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http://dx.doi.org/10.1016/j.jpurol.2020.02.003 | DOI Listing |
Adv Mater
January 2025
Division of Intelligent and Biomechanical Systems, State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Haidian, Beijing, 100084, China.
Quantitative assessment for post-stroke spasticity remains a significant challenge due to the encountered variable resistance during passive stretching, which can lead to the widely used modified Ashworth scale (MAS) for spasticity assessment depending heavily on rehabilitation physicians. To address these challenges, a high-force-output triboelectric soft pneumatic actuator (TENG-SPA) inspired by a lobster tail is developed. The bioinspired TENG-SPA can generate approximately 20 N at 0.
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January 2025
Institute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
It is a great challenge for a safe surgery to localize the cutting tip during laminar grinding. To address this problem, we develop a framework of state estimation based on the CT image-force model. For the proposed framework, the pre-operative CT image and intra-operative milling force signal work as source inputs.
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January 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.
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December 2024
Fakultät 1, Brandenburgische Technische Universität Cottbus-Senftenberg, Siemens-Halske-Ring 14, 03046 Cottbus, Germany.
Robot calibration and modelling measurements are commonly performed using a laser tracker. To capture three-dimensional positions, a SMR is attached to the robot. While some researchers employ adhesive bonds for this purpose, such methods often result in inaccurate, unstable and non-repeatable SMR positioning, adversely affecting measurement precision and the traceability of research outcomes.
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December 2024
Department of Computer Science & Artificial Intelligence, Jeonbuk National University, Jeonju-si 54896, Republic of Korea.
Recently, computer vision methods have been widely applied to agricultural tasks, such as robotic harvesting. In particular, fruit harvesting robots often rely on object detection or segmentation to identify and localize target fruits. During the model selection process for object detection, the average precision (AP) score typically provides the de facto standard.
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