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Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.

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Hand osteoarthritis (HOA) is a heterogeneous joint disease with high radiographic and symptomatic prevalence. The diagnosis of HOA is based on clinical and radiographic features. The identification of potential biomarkers for diagnosis, prognosis, disease severity assessment, and therapeutic efficacy evaluation of НОА remains an active area of research.

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Purpose: Hinge fractures show a relatively high incidence in varus and valgus distal femoral osteotomy (DFO) and can lead to delayed- or non-union. Another observed complication of a hinge fracture is an unintentional change of the postoperative femoral torsion of up to + 9.5° in conventionally performed DFO.

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Diffuse idiopathic skeletal hyperostosis (DISH) is a common disease in older adults that causes extensive spinal ankylosis. However, its impact on quality of life (QOL) and locomotive syndrome (LS) remains unknown. Thus, we aimed to evaluate the DISH effect on QOL and LS in community-dwelling middle-aged and older adults.

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