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Introduction: Implementing new approaches or new implants is always related with a certain learning curve in total hip arthroplasty (THA). Currently, many surgeons are switching to minimally invasive approaches combined with short stems for performing THA. Therefore, we aimed to asses and compare the learning curve of switching from an anterolateral Watson Jones approach (ALA) to a direct anterior approach (DAA) with the learning curve of switching from a neck-resecting to a partially neck-sparing short stem in cementless THA.
Materials And Methods: The first 150 consecutive THA performed through a DAA (Group A) and the first 150 consecutive THA using a partially neck-sparing short stem (Group B) performed by a single surgeon were evaluated within this retrospective cohort study. All cases were screened for surgery related adverse events (AE). Furthermore, the operative time of each surgery was evaluated and the learning curve assessed performing a cumulative sum (CUSUM) analysis.
Results: Overall, significantly more AE occurred in Group A compared to Group B (18.0% vs. 10.0%; p = 0.046). The sub-analysis of the AE revealed higher rates of periprosthetic joint infections (2.7% vs. 0.7%; p = 0.176), periprosthetic fractures (4.0% vs. 2.0%; p = 0.310) and overall revisions (4.7% vs. 1.3% p = 0.091) within Group A without statistical significance. The CUSUM analysis revealed a consistent reduction of operative time after 97 cases in Group A and 79 cases in Group B.
Conclusion: A significantly higher overall rate of AE was detected while switching approach compared to switching implant for performing THA. However, according to the results of this study, surgeons should be aware of the learning curve of the adoption to a new implant with different fixation philosophy as well.
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http://dx.doi.org/10.1007/s00402-024-05518-9 | 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.