Purpose: This study assessed the accuracy of cup and stem positioning and limb length adjustment for developmental dysplasia of the hip (DDH) using our new mechanical technique compared with imageless navigation or a computed tomography (CT)-based navigation system.
Methods: One hundred thirteen primary total hip arthroplasties (THAs) for DDH were evaluated. At pre-operative positioning, patients were placed in a precise lateral decubitus position by tilting the surgical table using simple ready-made devices (two shot pipe, metal chain, level gauge and goniometer). During surgery, cups were intentionally placed at 45° inclination and 15° anteversion on radiograph by using a level gauge and goniometer.
Results: Cup inclination was 44.2° ± 3.4° (range, 32.0-51.2°), cup anteversion was 19.6° ± 6.1° (range, 3.0-33.1°), stem alignment was 0.04° ± 0.8° valgus (range, 2.1° varus to 1.9° valgus), and leg length discrepancy was -0.37 ± 3.7 mm (range, -12.8 to 8.8 mm) in postoperative radiographs. Outliers (outside ±10° from intentional position) occurred in 15 cases (13.3 %) in inclination or anteversion. Postoperative dislocation did not occur in any cases.
Conclusions: Cup and stem positioning in THAs with our new mechanical technique yielded satisfactory results compared with previously reported imageless navigation or CT-based navigation. Our results were superior with regard to being non-invasive and low cost and involving minimum radiation exposure.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00264-014-2613-6 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Application of the 2-archive multi-objective cuckoo search algorithm for structure optimization.
Sci Rep
December 2024
Department of Electrical and Electronics Engineering, Engineering Faculty, Düzce University, Düzce, Turkey.
The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously.
View Article and Find Full Text PDFNano-second pulsed laser ablation of inconel 718 and MMPCD for simultaneous optimal ablation rate and surface quality.
Sci Rep
December 2024
Department of Mechanical Engineering, School of Science and Engineering, The American University in Cairo, AUC Avenue, 11835, New Cairo, Egypt.
This study investigates the ablation performance of Inconel 718, a nickel-based superalloy, and metal matrix polycrystalline diamond (MMPCD), a super composite, using a nano-second (ns) pulsed laser across a range of ablation conditions. Single trenches varying in energy fluence and scanning speeds were created, analyzing the experimental responses in terms of ablation rate and surface roughness. Using regression techniques, models were developed to understand these relationships.
View Article and Find Full Text PDFA deep transfer learning based convolution neural network framework for air temperature classification using human clothing images.
Sci Rep
December 2024
Department of Electrical Engineering, College of Engineering, Taif University, P.O. BOX 11099, 21944, Taif, Saudi Arabia.
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy.
View Article and Find Full Text PDFElucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations.
Nat Commun
December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
View Article and Find Full Text PDFA lightweight and precision dual track 1D and 2D feature fusion convolutional network for machinery equipment fault diagnosis.
Sci Rep
December 2024
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China.
Addressing the issues of a single-feature input channel structure, scarcity of training fault data, and insufficient feature learning capabilities in noisy environments for intelligent diagnostic models of mechanical equipment, we propose a method based on a one-dimensional and two-dimensional dual-channel feature information fusion convolutional neural network (1D_2DIFCNN). By constructing a one-dimensional and two-dimensiona dual-channel feature information fusion convolutional network and introducing a Convolutional Block Attention Mechanism, we utilize Random Overlapping Sampling Technique to process raw vibration signals. The model takes as inputs both one-dimensional data and two-dimensional Continuous Wavelet Transform images.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!