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Background: Laparoscopic suturing is an advanced skill that is difficult to acquire. Simulator-based skills curricula have been developed that have been shown to transfer to the operating room. Currently available skills curricula need to be optimized. We hypothesized that mastering basic laparoscopic skills first would shorten the learning curve of a more complex laparoscopic task and reduce resource requirements for the Fundamentals of Laparoscopic Surgery suturing curriculum.
Study Design: Medical students (n = 20) with no previous simulator experience were enrolled in an IRB-approved protocol, pretested on the Fundamentals of Laparoscopic Surgery suturing model, and randomized into 2 groups. Group I (n = 10) trained (unsupervised) until proficiency levels were achieved on 5 basic tasks; Group II (n = 10) received no basic training. Both groups then trained (supervised) on the Fundamentals of Laparoscopic Surgery suturing model until previously reported proficiency levels were achieved. Two weeks later, they were retested to evaluate their retention scores, training parameters, instruction requirements, and cost between groups using t-test.
Results: Baseline characteristics and performance were similar for both groups, and 9 of 10 subjects in each group achieved the proficiency levels. The initial performance on the simulator was better for Group I after basic skills training, and their suturing learning curve was shorter compared with Group II. In addition, Group I required less active instruction. Overall time required to finish the curriculum was similar for both groups; but the Group I training strategy cost less, with a savings of $148 per trainee.
Conclusions: Teaching novices basic laparoscopic skills before a more complex laparoscopic task produces substantial cost savings. Additional studies are needed to assess the impact of such integrated curricula on ultimate educational benefit.
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http://dx.doi.org/10.1016/j.jamcollsurg.2009.12.015 | DOI Listing |
BMC Oral Health
September 2025
Oral and Maxillofacial Radiology Department, Cairo university, Cairo, Egypt.
Aim: The purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans.
Methodology: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing.
J Robot Surg
September 2025
Department of General Surgery, Giglio Hospital Foundation, Cefalu', Italy.
The adoption of robotic pancreatectomy has grown significantly in recent years, driven by its potential advantages in precision, minimally invasive access, and improved patient recovery. However, mastering these complex procedures requires overcoming a substantial learning curve, and the role of structured mentoring in facilitating this transition remains underexplored. This systematic review and meta-analysis aimed to comprehensively evaluate the number of cases required to achieve surgical proficiency, assess the impact of mentoring on skill acquisition, and analyze how outcomes evolve throughout the learning process.
View Article and Find Full Text PDFMed Eng Phys
October 2025
Biomedical Device Technology, Istanbul Aydın University, Istanbul, 34093, Istanbul, Turkey. Electronic address:
Deep learning approaches have improved disease diagnosis efficiency. However, AI-based decision systems lack sufficient transparency and interpretability. This study aims to enhance the explainability and training performance of deep learning models using explainable artificial intelligence (XAI) techniques for brain tumor detection.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
September 2025
From the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America (J.S.S., B.M., S.H., A.H., J.S.), and Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India (H.S.).
Background And Purpose: The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies, these choroidal metastases are often missed. To improve their detection, we aimed to use artificial intelligence to distinguish between brain MRI scans containing normal orbits and choroidal metastases.
Materials And Methods: We present a novel hierarchical deep learning framework for sequential cropping and classification on brain MRI images to detect choroidal metastases.
Am J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.