AIMC Topic: Osteoarthritis, Knee
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Generating synthetic past and future states of Knee Osteoarthritis radiographs using Cycle-Consistent Generative Adversarial Neural Networks.
Computers in biology and medicine
Feb 9, 2025
Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limita...
Prediction of the Serial Alignment Change after Opening-Wedge High Tibial Osteotomy Based on Coronal Plane Alignment of the Knee Using Machine Learning Algorithm.
The journal of knee surgery
Jan 27, 2025
Categorization of alignment into phenotypes can be useful for predicting and analyzing postoperative alignment changes after opening-wedge high tibial osteotomy (OWHTO). The purposes of this study were to (1) develop a machine learning model for the ...
LMSST-GCN: Longitudinal MRI sub-structural texture guided graph convolution network for improved progression prediction of knee osteoarthritis.
Computer methods and programs in biomedicine
Jan 13, 2025
BACKGROUND AND OBJECTIVES: Accurate prediction of progression in knee osteoarthritis (KOA) is significant for early personalized intervention. Previous methods commonly focused on quantifying features from a specific sub-structure imaged at baseline ...
Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models.
Scientific reports
Jan 11, 2025
Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning ...
Predicting rapid progression in knee osteoarthritis: a novel and interpretable automated machine learning approach, with specific focus on young patients and early disease.
Annals of the rheumatic diseases
Jan 2, 2025
OBJECTIVES: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a ...
Knee osteoarthritis severity detection using deep inception transfer learning.
Computers in biology and medicine
Dec 31, 2024
Osteoarthritis (OA) is a prevalent condition resulting in physical limitations. Early detection of OA is critical to effectively manage this condition. However, the diagnosis of early-stage arthritis remains challenging. The Kellgren and Lawrence (KL...
Enhancing knee osteoarthritis diagnosis with DMS: a novel dense multi-scale convolutional neural network approach.
Journal of orthopaedic surgery and research
Dec 19, 2024
BACKGROUND: Osteoarthritis (OA) of the knee is a prevalent chronic degenerative joint condition that is having a growing impact on a global scale., posing a challenge in diagnosis which is often reliant on time-consuming and error-prone visual analys...
XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study.
Arthritis research & therapy
Dec 19, 2024
OBJECTIVE: To use routine demographic and clinical data to develop an interpretable individual-level machine learning (ML) model to diagnose knee osteoarthritis (KOA) and to identify highly ranked features.
Advances in Artificial Intelligence for automated knee osteoarthritis classification using the IKDC system.
European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
Dec 2, 2024
INTRODUCTION: Knee osteoarthritis is one of the most prevalent and debilitating musculoskeletal diseases, with a high incidence among the elderly population. Early detection and accurate classification can improve clinical outcomes for affected patie...
Machine learning is better than surgeons at assessing unicompartmental knee replacement radiographs.
The Knee
Nov 30, 2024
BACKGROUND: Poor results occasionally occur after unicompartmental knee replacement (UKR). It is often difficult, even for experienced surgeons, to determine why patients have poor outcomes from radiographs. The aim was to compare the ability of expe...
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