Accurate component placement in knee replacement surgery is important. The precision with which the implants are placed directly affects patient outcome as implant position and alignment influence stability, durability and patellar tracking. The ability to measure the accuracy of implantation of knee replacement components is valuable in assessing not only ones own technique but also in evaluating new instruments or implants and in teaching. The standard AP and lateral radiographs employed by most surgeons give inadequate information to assess alignment of each component accurately. We present a straightforward way of assessing femoral and tibial component alignment by using a series of three radiographs. This technique is reproducible and can be performed using standard equipment in any radiology department. This technique was applied to 160 total knee replacements performed using newly developed instrumentation. It was shown to be simple and the measurements were reproducible, with very little inter observer bias. We believe this technique has a role in audit, teaching, training and assessing new techniques and instruments.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/s0968-0160(00)00078-8 | DOI Listing |
Arch Gynecol Obstet
January 2025
Department of Radiology, First People's Hospital of Shangqiu, Shangqiu, 476000, China.
Objective: To assess and compare the diagnostic accuracy of radiologist, MR findings, and radiomics-clinical models in the diagnosis of placental implantation disorders.
Methods: Retrospective collection of MR images from patients suspected of having placenta accreta spectrum (PAS) was conducted across three institutions: Institution I (n = 505), Institution II (n = 67), and Institution III (n = 58). Data from Institution I were utilized to form a training set, while data from Institutions II and III served as an external test set.
Front Bioeng Biotechnol
January 2025
Center for Orthopaedic Biomechanics, University of Denver, Denver, CO, United States.
Introduction: Accurate prediction of knee biomechanics during total knee replacement (TKR) surgery is crucial for optimal outcomes. This study investigates the application of machine learning (ML) techniques for real-time prediction of knee joint mechanics.
Methods: A validated finite element (FE) model of the lower limb was used to generate a dataset of knee joint kinematics, kinetics, and contact mechanics.
Sci Rep
January 2025
Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Kagawa University, 1750-1, Ikenobe, Miki-cho, Kita-gun, Takamatsu, 761-0793, Kagawa, Japan.
This study aims to evaluate the potential enhancement in implant classification performance achieved by incorporating artificially generated images of commercially available products into a deep learning process of dental implant classification using panoramic X-ray images. To supplement an existing dataset of 7,946 in vivo dental implant images, a three-dimensional scanner was employed to create implant surface models. Subsequently, implant surface models were used to generate two-dimensional X-ray images, which were compiled along with original images to create a comprehensive dataset.
View Article and Find Full Text PDFEar Hear
January 2025
San Francisco Department of Otolaryngology - Head and Neck Surgery, University of California, San Francisco, California, USA.
Objectives: Cochlear implant (CI) user functional outcomes are challenging to predict because of the variability in individual anatomy, neural health, CI device characteristics, and linguistic and listening experience. Machine learning (ML) techniques are uniquely poised for this predictive challenge because they can analyze nonlinear interactions using large amounts of multidimensional data. The objective of this article is to systematically review the literature regarding ML models that predict functional CI outcomes, defined as sound perception and production.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Bangkok Hospital Dental Center Holistic Care and Dental Implant, Bangkok Hospital, Bangkok, 10310, Thailand.
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.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!