Objectives: This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs).
Materials And Methods: This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Purpose: Juvenile scoliosis (JS), among different types of spinal deformity, remains still a challenge for orthopedic surgeons. Elongation, derotation and flexion (EDF) casting technique is a custom-made thoracolumbar cast based on a three-dimensional correction concept. The primary objective of the present study was to measure changes on plain radiographs of patients with JS treated with EDF plaster technique.
View Article and Find Full Text PDFBackground: The main objective of this study was to retrospectively evaluate the clinical and radiographic outcomes of displaced tibial shaft fractures with intact fibula in children after nonoperative management and operative treatment by elastic stable intramedullary nailing.
Methods: A study was performed on 80 consecutive children, 56 males, 24 females from 2 Institutions, with displaced and closed tibial shaft fracture with intact fibula. All patients underwent regular clinical and radiographic follow-up visits for at least 2 years after injury.