Background: Aim of our study was to evaluate learning curve of the Millin simple prostatectomy analyzing three expert laparoscopic surgeons.
Methods: Learning curve of 3 expert laparoscopic surgeons with excellent radical prostatectomy training was evaluated. Surgeon 1 had more than 20 years of experience, while other surgeons had 10 years of experience. The first 45 procedures of the surgeons were considered for analysis. The cumulative sum (CUSUM) technique, one of the methods developed to monitor the performance and quality of the industrial sector, was adopted to analyze learning curves. The variables included to evaluate learning curve of the surgeons were: operative time (OT), hospitalization (HO) and complication rate.
Results: Overall 135 patients were included in the analysis. Median age was 68 (64/74), median prostate volume was 83 (75/97), median Q
Conclusions: According to our results 15 procedures are needed to reach a plateau in surgical time for trained laparoscopic surgeons.
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http://dx.doi.org/10.23736/S2724-6051.21.04114-X | DOI Listing |
Invest Radiol
October 2024
From the Department of Radiology and Nuclear Medicine, UKSH Lübeck, Lübeck, Germany (J.S., M.M., L.B., Y.E., J.B., M.M.S.); Institute of Medical Informatics, University of Lübeck, Lübeck, Germany (L.H., M.P.H.); Philips Research Hamburg, Hamburg, Germany (A.S., H.S.); and Institute of Interventional Radiology, UKSH Lübeck, Lübeck, Germany (M.M.S.).
Purpose: Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations that lack clinician-friendly comprehensibility. This study aims to introduce an approach that employs segmentation of support material and anatomy to enhance the precision and comprehensibility of CVC misplacement detection.
View Article and Find Full Text PDFInt J Surg
October 2024
Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Objective: To develop a model for accurate prediction of axillary lymph node (LN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients with nodal involvement.
Methods: Between October 2018 and February 2024, 671 breast cancer patients with biopsy-proven LN metastasis who received NAC followed by axillary LN dissection were enrolled in this prospective, multicenter study. Preoperative ultrasound (US) images, including B-mode ultrasound (BUS) and shear wave elastography (SWE), were obtained.
Neurol Sci
December 2024
Department of Radiology, The First People's Hospital of Foshan, #81 North Lingnan Avenue, Foshan, Guangdong, China.
Background: Identifying Parkinson's disease (PD) during its initial phases presents considerable hurdles for clinicians.
Purpose: To examine the feasibility and efficacy of a machine learning model based on quantitative multiparametric magnetic resonance imaging (MRI) features in identifying early-stage PD.
Methods: We recruited 33 participants, including 19 with early-stage PD, 14 with advanced-stage PD and 20 healthy control subjects.
PLoS One
December 2024
Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Background And Purpose: External drainage represents a well-established treatment option for acute intracerebral hemorrhage. The current standard of practice includes post-operative computer tomography imaging, which is subjectively evaluated. The implementation of an objective, automated evaluation of postoperative studies may enhance diagnostic accuracy and facilitate the scaling of research projects.
View Article and Find Full Text PDFJAMA Oncol
December 2024
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Importance: Only a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to benefit from immunotherapy.
Objective: To develop a supervised deep learning-based ICI response prediction method; evaluate its performance alongside other known predictive biomarkers; and assess its association with clinical outcomes in patients with advanced NSCLC.
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