Generating synthetic past and future states of knee osteoarthritis radiographs using cycle-consistent generative adversarial neural networks
10.1016/j.compbiomed.2025.109785 · doi-reference
TurkerNeXtV2: an innovative CNN model for knee osteoarthritis pressure image classification
10.3390/diagnostics15192478 · doi-reference
Distributed deep learning networks among institutions for medical imaging
10.1093/jamia/ocy017 · doi-reference
Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method
10.1186/s13075-024-03416-4 · doi-reference
A multi-classifier for grading knee osteoarthritis using gait analysis
10.1016/j.patrec.2010.01.003 · doi-reference
A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis
10.1038/s41598-025-99460-4 · doi-reference
A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading
10.1039/d2ay01526a · doi-reference
Machine learning-based automatic classification of knee osteoarthritis severity using gait data and radiographic images
10.1109/access.2020.3006335 · doi-reference
Automatic classification of the severity of knee osteoarthritis using enhanced image sharpening and CNN
10.3390/app13031658 · doi-reference
Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: data from the osteoarthritis initiative
10.1016/j.media.2018.11.009 · doi-reference
Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry
10.1148/radiol.2018172322 · doi-reference
OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-ray imaging
10.3389/fbioe.2024.1437188 · doi-reference
Knee osteoarthritis automatic detection using U-Net
10.11591/ijai.v13.i2.pp2122-2130 · doi-reference
Knee osteoarthritis classification using X-ray images based on optimal deep neural network
10.32604/csse.2023.040529 · doi-reference
Automatic grading of individual knee osteoarthritis features in plain radiographs using deep convolutional neural networks
10.3390/diagnostics10110932 · doi-reference
Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss
10.1016/j.compmedimag.2019.06.002 · doi-reference
A ResNet-based approach for accurate radiographic diagnosis of knee osteoarthritis
10.1049/cit2.12079 · doi-reference
Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach
10.1038/s41598-018-20132-7 · doi-reference
Lightweight early detection of knee osteoarthritis in athletes
10.1038/s41598-025-04095-0 · doi-reference
Gradient-based learning applied to document recognition
10.1109/5.726791 · doi-reference
Machine-learning-based patient-specific prediction models for knee osteoarthritis
10.1038/s41584-018-0130-5 · doi-reference
DeepKOA: a deep-learning model for predicting progression in knee osteoarthritis using multimodal magnetic resonance images from the osteoarthritis initiative
10.21037/qims-22-1251 · doi-reference
XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study
10.1186/s13075-024-03450-2 · doi-reference
Detecting knee osteoarthritis and its discriminating parameters using random forests
10.1016/j.medengphy.2017.02.004 · doi-reference
Improved prediction of knee osteoarthritis by the machine learning model XGBoost
10.1007/s43465-023-00936-0 · doi-reference
Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images
10.1038/s41598-019-42215-9 · doi-reference
Development of an automated optimal distance feature-based decision system for diagnosing knee osteoarthritis using segmented X-ray images
10.1016/j.heliyon.2023.e21703 · doi-reference
Automatic detection and classification of knee osteoarthritis using deep learning approach
10.1007/s11547-022-01476-7 · doi-reference
Emergence of deep learning in knee osteoarthritis diagnosis
10.1155/2021/4931437 · doi-reference
The value of deep learning-based X-ray techniques in detecting and classifying KL grades of knee osteoarthritis: a systematic review and meta-analysis
10.1007/s00330-024-10928-9 · doi-reference
A ROS-responsive hydrogel encapsulated with matrix metalloproteinase-13 siRNA nanocarriers to attenuate osteoarthritis progression
10.1186/s12951-024-03046-7 · doi-reference
A comparative study of machine learning classifiers for enhancing knee osteoarthritis diagnosis
10.3390/info15040183 · doi-reference
Radiological assessment of osteo-arthrosis
10.1136/ard.16.4.494 · doi-reference
Identifying severity grading of knee osteoarthritis from x-ray images using an efficient mixture of deep learning and machine learning models
10.3390/diagnostics12122939 · doi-reference
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists
10.1016/j.compbiomed.2021.104334 · doi-reference
Knee osteoarthritis detection and classification using X-rays
10.1109/access.2023.3276810 · doi-reference
