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| http://dx.doi.org/10.1002/bco2.373 | DOI Listing |
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Comparing prediction accuracy for 30-day readmission following primary total knee arthroplasty: the ACS-NSQIP risk calculator versus a novel artificial neural network model.
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Bioengineering Laboratory, Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Background: Unplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility.
View Article and Find Full Text PDFImplementation strategies: lessons learned during an e-learning intervention to improve dietary behaviors and feeding practices in early childhood education and care.
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Centre for Lifecourse Nutrition, Department of Nutrition and Public Health, Faculty of Health and Sport Sciences, University of Agder, Postbox 422, Kristiansand, 4604, Norway.
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Department of Magnetic Resonance Imaging, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China.
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Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.
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View Article and Find Full Text PDFA novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.
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Department of Software Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, 4800, Turkey.
Objectives: Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.
Materials And Methods: The first stage involved tooth localization with the YOLOv8 model, followed by pulp stone classification using ResNeXt.
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