Objective: To introduce a quality improvement initiative tracking robotic instrument failures on a per case basis. It is imperative to understand rates of failure, financial implications of failures, and identify factors suggesting common mechanisms of failure.
Materials And Methods: Starting in January 1, 2014 a quality reporting system for failed robotic equipment began. Staff was instructed to submit an incident report when a robotic instrument failed and the instrument returned to central processing. Instruments were then returned to the manufacturer (Intuitive Surgical Inc, Sunnyvale, CA) for analysis and reimbursement. Results of failure analysis by the manufacturer, including reimbursement rates, were recorded and correlated with the procedure and surgical specialty.
Results: A total of 3935 robotic cases were performed during the study period with a reported instrument failure incidence of 6.2% (247 total instruments). Etiology of instrument failure was as follows: tip or wrist (46.9%), cable (30.0%), unknown (12.6%), control housing (5.3%), and shaft (3.2%). Highest instrument failure incidence was seen in colorectal surgery cases at 4.0%, Urology had the lowest at 2.7%. Manufacturer reimbursement rate was 57.9%; the most common reason for denial being mishandling/misuse of equipment, determined by manufacturer analysis.
Conclusion: Herein, we have demonstrated that improved process flow of reporting is necessary to better track incidence and etiology of instrument failures. Cost savings comes from improved training of not only surgeons but operating room and central processing staff in handling equipment to prevent high rates of reimbursement denial.
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http://dx.doi.org/10.1016/j.urology.2019.02.052 | DOI Listing |
Sensors (Basel)
January 2025
Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing Institute of Agricultural Mechanization, Nanjing 210014, China.
To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of , in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate .
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January 2025
Centre for Automation and Robotics (CAR UPM-CSIC), Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.
Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations.
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January 2025
School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety.
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January 2025
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
This paper aims to address the challenge of precise robotic grasping of molecular sieve drying bags during automated packaging by proposing a six-dimensional (6D) pose estimation method based on an red green blue-depth (RGB-D) camera. The method consists of three components: point cloud pre-segmentation, target extraction, and pose estimation. A minimum bounding box-based pre-segmentation method was designed to minimize the impact of packaging wrinkles and skirt curling.
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January 2025
The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.
Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments. However, traditional video instance segmentation methods face complex models, high computational overheads, and slow segmentation speeds in time-series feature extraction, especially in resource-constrained environments.
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