Objective: To assess the accuracy and precision of a software-aided system to measure migration of femoral components after total hip replacement (THR) on digitised radiographs.
Design And Patients: Subsidence and varus-valgus tilt of THR stems within the femur were measured on digitised anteroposterior pelvic radiographs. The measuring software (UMA, GEMED, Germany) relies on bony landmarks and comparability parameters of two consecutive radiographs. Its accuracy and precision were calculated by comparing it with the gold standard in migration measurements, radiostereometric analysis (RSA). Radiographs and corresponding RSA measurements were performed in 60 patients (38-69 years) following cementless THR surgery.
Results And Conclusions: The UMA software measured the subsidence of the stems with an accuracy of +/-2.5 mm and varus-valgus tilt with an accuracy of +/-1.8 degrees (95% confidence interval). A good interobserver and intraobserver reliability was calculated with Cronbach's alpha ranging from 0.86 to 0.97. Measuring the subsidence of THR stems within the femur is an important parameter in the diagnosis of implant loosening. Software systems such as UMA improve the accuracy of migration measurements and are easy to use on routinely performed radiographs of operated hip joints.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00256-003-0670-9 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A vision model for automated frozen tuna processing.
Sci Rep
January 2025
School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan, 316022, People's Republic of China.
Accurate and rapid segmentation of key parts of frozen tuna, along with precise pose estimation, is crucial for automated processing. However, challenges such as size differences and indistinct features of tuna parts, as well as the complexity of determining fish poses in multi-fish scenarios, hinder this process. To address these issues, this paper introduces TunaVision, a vision model based on YOLOv8 designed for automated tuna processing.
View Article and Find Full Text PDFPotential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer.
Sci Rep
January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
View Article and Find Full Text PDFA quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.
Sci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFEfficient diagnosis of diabetes mellitus using an improved ensemble method.
Sci Rep
January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
View Article and Find Full Text PDFColorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.
Sci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
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!