Objective: An understanding of the learning curve of a new surgical procedure is essential for its safe clinical integration, teaching, and assessment. This knowledge is currently deficient for lumbar microendoscopic discectomy (MED). The present article aims to profile the learning curve for MED of an individual surgeon in a hospital not previously exposed to this procedure.
Methods: The first 35 cases of MED for posterolateral lumbar disc prolapse causing radiculopathy performed at the Princess Alexandra Hospital, Brisbane, Australia, were studied prospectively. The learning curve was assessed using surgery time, conversion rate, complication rate, surgeon "comfort," and key learning steps.
Results: The duration of surgical operating time decreased over the course of the study, initially rapidly and then more gradually. There were three conversions to open discectomy in the first 7 cases and none in the next 28 cases. The complexity of cases increased over the series, and the complication rate decreased. The asymptote of the learning curve seems to be approximately 30 cases. The specific learning tasks of MED include lateral lamina radiology, scope vision, visuospatial orientation, smaller field of view, angle of approach and tube position, and care and handling of endoscope equipment.
Conclusion: A learning curve for MED has been demonstrated. Further assessment of this curve for a population of surgeons is necessary before a clinical assessment of open discectomy versus MED can be embarked upon.
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http://dx.doi.org/10.1227/01.neu.0000156470.79032.7b | DOI Listing |
Arthroplast Today
February 2025
Adult Reconstruction and Joint Replacement Service, Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA.
Background: Robotic arm assisted total knee arthroplasty (RA-TKA) aims to improve accuracy in bone resection, implant positioning, and joint alignment compared to manual TKA (M-TKA). However, the learning curve of RA-TKA can disrupt operating room efficiency, increase complications, and raise costs. This study examines the operative time learning curve of RA-TKA using a single robotic system.
View Article and Find Full Text PDFIntroduction: Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains limited. We aimed to develop a deep-learning algorithm using OCT to detect AD and MCI.
Methods: We performed a cross-sectional study involving 228 Asian participants (173 cases/55 controls) for model development and testing on 68 Asian (52 cases/16 controls) and 85 White (39 cases/46 controls) participants.
iScience
January 2025
The First Clinical College, Shandong University of Traditional Chinese Medicine, Jinan 250013, P.R. China.
The advancement of information technology and AI has boosted global economic and social development. Robot systems (RS) and computer-aided technology (CAT) are used in various domains, including social production and human existence. Traditional fracture reduction surgery relies on the expertise and surgical skills of surgeons to realign fractures in patients.
View Article and Find Full Text PDFHeliyon
January 2025
Chest Clinical College of Tianjin Medical University, Tianjin, 300270, China.
Backgroud: Fluid volume abnormalities are a major cause of exacerbations in heart failure patients. However, there is few efficient, rapid, or cost-effective clinical approach for determining volume status, resulting in inadequate or unsatisfactory treatment. The aim was to develop an early fluid volume detection model for heart failure patients utilizing a machine learning stratification.
View Article and Find Full Text PDFOphthalmol Sci
November 2024
Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Purpose: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.
Design: Retrospective observational study.
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