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A Deep Learning Framework for Automated Classification and Archiving of Orthodontic Diagnostic Documents.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFPredicting rapid progression in knee osteoarthritis: a novel and interpretable automated machine learning approach, with specific focus on young patients and early disease.
Ann Rheum Dis
January 2025
Department of Surgery, University of Cambridge, Cambridge, UK.
Objectives: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period.
Methods: We developed autoML models integrating clinical, biochemical, X-ray and MRI data. Using two data sets within the OA Initiative-the Foundation for the National Institutes of Health OA Biomarker Consortium for training and hold-out validation, and the Pivotal Osteoarthritis Initiative MRI Analyses study for external validation-we employed two distinct definitions of clinical outcomes: Multiclass (categorising OA progression into pain and/or radiographic) and binary.
Construction and validation of a nomogram prediction model for the occurrence of complications in patients following robotic radical surgery for gastric cancer.
Langenbecks Arch Surg
January 2025
Department of General Surgery, Gansu Provincial Hospital, Lanzhou, 730000, China.
Background: In the last two decades, robotic-assisted gastrectomy has become a widely adopted surgical option for gastric cancer (GC) treatment. Despite its popularity, postoperative complications can significantly deteriorate patient quality of life and prognosis. Therefore, identifying risk factors for these complications is crucial for early detection and intervention.
View Article and Find Full Text PDFNatural Causes of Sports- and Recreation-Related Deaths in the General Population: A 14-Year Review in Québec, Canada.
CJC Open
January 2025
Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Québec, Canada.
Background: This study analyzed trends in the frequencies and rates of natural deaths associated with sport and recreation activities in Québec, Canada, from January 2006 to December 2019, and investigated their etiology and characteristics.
Methods: This descriptive retrospective study utilized data from coroner reports, as well as autopsy and police reports. Activity-specific incidence rates were calculated using participation data from the (ÉBARS) and Canadian census population data.
Influence of matrix stiffness on microstructure evolution and magnetization of magneto-active elastomers.
Soft Matter
January 2025
Leibniz-Institut für Polymerforschung Dresden e.V., Hohe Strasse 6, Dresden, 01069, Germany.
Field-induced microstructure evolution can play an important role in defining the coupled magneto-mechanical response of Magneto-Active Elastomers (MAEs). The behavior of these materials is classically modeled using mechanical, magnetic and coupled magneto-mechanical contributions to their free energy function. If the MAE sample is fully clamped so it cannot deform, the mechanical coupling is reduced to the internal microscopic deformations caused by the particles moving and deforming the elastic medium that surrounds them.
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