Application-Specific Learning Curve With a Modern Computer-Assisted Orthopedic Surgery System for Joint Arthroplasty

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Yifei Dai ◽  
Sharat Kusuma ◽  
Alexander T. Greene ◽  
Wen Fan ◽  
Amaury Jung ◽  
...  

Abstract A commonly acknowledged barrier for the adoption of new computer-assisted orthopedic surgery (CAOS) technologies relates to a perceived long and steep learning curve. However, this perception has not been objectively tested with the consideration of surgeon-specific learning approaches. This study employed the cumulative sum control chart (CUSUM) to investigate individual surgeon's learning of CAOS technology by monitoring the stability of the surgical process regarding surgical time. Two applications for total knee arthroplasty (TKA) and two applications for total shoulder arthroplasty (TSA) provided by a modern CAOS system were assessed with a total of 21 surgeons with different levels of previous CAOS experience. The surgeon-specific learning durations identified by CUSUM method revealed that CAOS applications with “full guidance” (i.e., those that offer comprehensive guidance, full customization, and utilize CAOS-specific instrumentation) required on average less than ten cases to learn, while the streamlined application designed as a CAOS augmentation of existing mechanical instrumentation demonstrated a minimal learning curve (less than three cases). During the learning phase, the increase in surgical time was found to be moderate (approximately 15 min or less) for the “full guidance” applications, while the streamlined CAOS application only saw a clinically negligible time increase (under 5 min). The CUSUM method provided an objective and consistent measurement on learning, and demonstrated, contrary to common perception, a minimal to modest learning curve required by the modern CAOS system studied.

10.29007/nrzj ◽  
2020 ◽  
Author(s):  
Yifei Dai ◽  
Laurent Angibaud ◽  
Guillaume Bras ◽  
Cyril Hamad ◽  
Jefferson Craig Morrison

This study employed an advanced method (CUSUM) to analyze the learning curve regarding surgical efficiency (time) using two CAOS applications, which were designed to address user needs with different levels of comprehensiveness in term of offered guidance and instrumentation requirements. Two group of surgeons, each used either CAOS applications were included in the study. The first 50 CAOS TKA cases from each surgeon were analyzed to identify the learning curve. The duration of learning, as well as the impact of learning based on surgical time, were assessed with regard to the specific CAOS application and surgeon’s previous CAOS experience level. The data demonstrated differences in term of pattern of adoption during learning process between the two CAOS applications. However, the learning process was not sensitive to surgeon’s experience level.


10.29007/clwq ◽  
2018 ◽  
Author(s):  
Guillaume Dardenne ◽  
Zoheir Dib ◽  
Chafiaa Hamitouche ◽  
Christian Lefèvre ◽  
Eric Stindel

Functional approaches for the localization of the hip center (HC) are widely used in Computer Assisted Orthopedic Surgery (CAOS). These methods aim to compute the HC defined as the center of rotation (CoR) of the femur with respect to the pelvis. The Least-Moving-Point (LMP) method is one approach which consists in detecting the point that moves the least during the circumduction motion. The goal of this paper is to highlight the limits of the native LMP (nLMP) and to propose a modified version (mLMP). A software application has been developed allowing the simulation of a circumduction motion of a hip in order to generate the required data for the computation of the HC. Two tests have been defined in order to assess and compare both LMP methods with respect to (1) the camera noise (CN) and (2) the acetabular noise (AN). The mLMP and nLMP error is respectively: (1) 0.5±0.2mm and 9.3±1.4mm for a low CN, 21.7±3.6mm and 184.7±13.1mm for a high CN, and (2) 2.2±1.2mm and 0.5±0.3mm for a low AN, 35.2±18.5mm and 13.0±8.2mm for a high AN. In conclusion, mLMP is more robust and accurate than the nLMP algorithm.


2017 ◽  
pp. 333-423
Author(s):  
Hong Gao ◽  
Sang Hongxun ◽  
Cheng Bin ◽  
Wu Zixiang ◽  
Fan Yong ◽  
...  

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