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.
Methods: The study analyzed the first 80 RA-TKA and the last 80 M-TKA cases performed by a single surgeon using the VELYS robotic system after transitioning from M-TKA. Cases were subdivided into groups of 20 and compared to M-TKA cases. A cumulative summation analysis identified the learning curve phases.
Results: Three phases were identified: Phase 1 (initial learning, cases 1-9), Phase 2 (increased competence, plateau from cases 10-52), and Phase 3 (post-learning, optimized performance from cases 53-80). Mean surgical time for RA-TKA was 42.4 ± 8.7 minutes, compared to 35.3 ± 7.0 minutes for M-TKA ( < .001). Early RA-TKA cases (1-20) had significantly longer times than late RA-TKA cases (61-80) and M-TKA cases ( < .05). Late RA-TKA times were comparable to M-TKA ( = .06).
Conclusions: RA-TKA is an enabling surgical tool that can be integrated efficiently into a surgical workflow with a rapid learning curve of 9 cases.
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http://dx.doi.org/10.1016/j.artd.2024.101588 | DOI Listing |
Asian Pac J Cancer Prev
January 2025
Department of Nuclear Medicine, Busan Paik Hospital, University of Inje College of Medicine, Busan, Republic of Korea.
Objective: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and clinical information for predicting regional lymph node metastasis in patients with colon cancer.
Methods: This retrospective study included 193 patients diagnosed with colon cancer between January 2014 and December 2017. All patients underwent F-18 FDG PET/CT and blood test before surgery.
Radiology
January 2025
From the Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Fl, Rm 313, New York, NY 10016 (S.S.W., J.V., R.K., E.H.P., J.F.); Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, University Hospital Tübingen, Tübingen, Germany (S.S.W.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V.); Department of Radiology, Hospital do Coraçao, São Paulo, Brazil (T.C.R.); Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), London, United Kingdom (D.D.); Department of Radiology, Balgrist University Hospital, Zurich, Switzerland (B.F.); Department of Radiology, Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Medscanlagos Radiology, Cabo Frio, Brazil (A.S.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Siemens Healthineers AG, Erlangen, Germany (I.B.); and Siemens Medical Solutions USA, Malvern, Pa (G.K.).
Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (SMSx2) twofold-parallel-imaging (PIx2)-accelerated DL superresolution MRI in the knee against conventional SMSx2-PIx2-accelerated MRI using arthroscopy as the reference standard. Materials and Methods Adults with painful knee conditions were prospectively enrolled from December 2021 to October 2022.
View Article and Find Full Text PDFFront Cardiovasc Med
January 2025
Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
Objective: We aimed to explore the application value of unsupervised machine learning in identifying acute gastrointestinal injury (AGI) after extracorporeal circulation for acute type A aortic dissection (ATAAD).
Methods: Patients who underwent extracorporeal circulation for ATAAD at the First Hospital of Lanzhou University from January 2016 to January 2021 were included. Unsupervised machine learning algorithm was used to stratify patients into different phenogroups according to the similarity of their clinical features and laboratory test results.
Purpose: Robotic-assisted total knee arthroplasty (RA-TKA) has gained popularity for its potential ability to improve surgical precision and patient outcomes, despite concerns about its long learning curve and increased operative times. The aim of this study is to evaluate the learning curve of the ROSA® Knee System, the relationship between each phase of the learning curve and the accuracy of the robotic system in femoral component size and knee alignment prediction.
Methods: A single surgeon retrospective analysis of total operative time (TOT) and total robotic time was conducted.
World J Gastrointest Surg
January 2025
General Surgery Center, General Hospital of Western Theater Command, Chengdu 610000, Sichuan Province, China.
Background: Minimally invasive pancreaticoduodenectomy (MIPD) is considered one of the most complex procedures in general surgery. The number of articles on MIPD has been increasing annually. However, published reports often have complex research directions, and the focal points frequently change.
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