142 research outputs found
A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges
Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits
Automatic diagnosis of knee osteoarthritis severity using Swin transformer
Knee osteoarthritis (KOA) is a widespread condition that can cause chronic
pain and stiffness in the knee joint. Early detection and diagnosis are crucial
for successful clinical intervention and management to prevent severe
complications, such as loss of mobility. In this paper, we propose an automated
approach that employs the Swin Transformer to predict the severity of KOA. Our
model uses publicly available radiographic datasets with Kellgren and Lawrence
scores to enable early detection and severity assessment. To improve the
accuracy of our model, we employ a multi-prediction head architecture that
utilizes multi-layer perceptron classifiers. Additionally, we introduce a novel
training approach that reduces the data drift between multiple datasets to
ensure the generalization ability of the model. The results of our experiments
demonstrate the effectiveness and feasibility of our approach in predicting KOA
severity accurately.Comment: CBMI 202
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach
Large numbers of radiographic images are available in knee radiology
practices which could be used for training of deep learning models for
diagnosis of knee abnormalities. However, those images do not typically contain
readily available labels due to limitations of human annotations. The purpose
of our study was to develop an automated labeling approach that improves the
image classification model to distinguish normal knee images from those with
abnormalities or prior arthroplasty. The automated labeler was trained on a
small set of labeled data to automatically label a much larger set of unlabeled
data, further improving the image classification performance for knee
radiographic diagnosis. We developed our approach using 7,382 patients and
validated it on a separate set of 637 patients. The final image classification
model, trained using both manually labeled and pseudo-labeled data, had the
higher weighted average AUC (WAUC: 0.903) value and higher AUC-ROC values among
all classes (normal AUC-ROC: 0.894; abnormal AUC-ROC: 0.896, arthroplasty
AUC-ROC: 0.990) compared to the baseline model (WAUC=0.857; normal AUC-ROC:
0.842; abnormal AUC-ROC: 0.848, arthroplasty AUC-ROC: 0.987), trained using
only manually labeled data. DeLong tests show that the improvement is
significant on normal (p-value<0.002) and abnormal (p-value<0.001) images. Our
findings demonstrated that the proposed automated labeling approach
significantly improves the performance of image classification for radiographic
knee diagnosis, allowing for facilitating patient care and curation of large
knee datasets.Comment: This is the preprint versio
Automatic quantification of radiographic knee osteoarthritis severity and associated diagnostic features using deep convolutional neural networks
âAutomatic Quantification of Radiographic Knee Osteoarthritis Severity and Associated Diagnostic Features using Deep Convolutional Neural Networksâ A. Joseph Antony
Due to the increasing prevalence of knee Osteoarthritis (OA), a debilitating kneejoint degradation, and total joint arthoplasty as a serious consequence, there is a need for effective clinical and scientific tools to assess knee OA in its early stages. This thesis investigates the use of machine learning algorithms and deep learning architectures, in particular convolutional neural networks (CNN), to quantify the severity and clinical radiographic features of knee OA. The goal is to offer novel and effective solutions to automatically assess the severity of knee OA achieving on par with human accuracy. Instead of conventional hand-crafted features, it is proposed in this thesis that automatically learning features in a supervised manner can be more effective for fine-grained knee OA image classification.
The main contributions of this thesis are as follows. First, the use of off-the-shelf CNNs are investigated for classifying knee OA images through transfer learning by fine-tuning the CNNs. Second, CNNs are trained from scratch to quantify the knee OA severity optimising a weighted ratio of two loss functions: categorical cross entropy and mean-squared error. Third, CNNs are jointly trained to quantify the clinical features of knee OA: joint space narrowing (JSN) and osteophytes along with the KL grades. This improves the overall quantification of knee OA severity producing simultaneous predictions of KL grades, JSN and osteophytes. Two public datasets are used to evaluate the approaches, the OAI and the MOST, with extremely promising results that outperform existing approaches. In summary, this thesis primarily contributes to the field of automated methods for localisation and quantification of radiographic knee OA
Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification
Purpose: The aim of this study was to demonstrate the utility of unsupervised
domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype
classification using a small dataset (n=50). Materials and Methods: For this
retrospective study, we collected 3,166 three-dimensional (3D) double-echo
steady-state magnetic resonance (MR) images from the Osteoarthritis Initiative
dataset and 50 3D turbo/fast spin-echo MR images from our institute (in 2020
and 2021) as the source and target datasets, respectively. For each patient,
the degree of knee OA was initially graded according to the MRI Osteoarthritis
Knee Score (MOAKS) before being converted to binary OA phenotype labels. The
proposed UDA pipeline included (a) pre-processing, which involved automatic
segmentation and region-of-interest cropping; (b) source classifier training,
which involved pre-training phenotype classifiers on the source dataset; (c)
target encoder adaptation, which involved unsupervised adaption of the source
encoder to the target encoder and (d) target classifier validation, which
involved statistical analysis of the target classification performance
evaluated by the area under the receiver operating characteristic curve
(AUROC), sensitivity, specificity and accuracy. Additionally, a classifier was
trained without UDA for comparison. Results: The target classifier trained with
UDA achieved improved AUROC, sensitivity, specificity and accuracy for both
knee OA phenotypes compared with the classifier trained without UDA.
Conclusion: The proposed UDA approach improves the performance of automated
knee OA phenotype classification for small target datasets by utilising a
large, high-quality source dataset for training. The results successfully
demonstrated the advantages of the UDA approach in classification on small
datasets.Comment: Junru Zhong and Yongcheng Yao share the same contribution. 17 pages,
4 figures, 4 table
KNEE OSTEOARTHRITIS PREDICTION DRIVEN BY DEEP LEARNING AND THE KELLGREN-LAWRENCE GRADING
Degenerative osteoarthritis of the knee (KOA) affects the knee compartments and worsens over 10â15 years. Knee osteoarthritis is the major cause of activity restrictions and impairment in older persons. Clinicians' expertise affects visual examination interpretation. Hence, achieving early detection requires fast, accurate, and affordable methods. Deep learning (DL) convolutional neural networks (CNN) are the most accurate knee osteoarthritis diagnosis approach. CNNs require a significant amount of training data. Knee X-rays can be analyzed by models that use deep learning to extract the features and reduce number of training cycles. This study suggests the usage of DL system that is based on a trained network on five-class knee X-rays with VGG16, SoftMax (Normal, Doubtful, Mild, Moderate, Severe). Two deep CNNs are used to grade knee OA instantly using the Kellgren-Lawrence (KL) methodology. The experimental analysis makes use of two sets of 1650 different knee X-ray images. Each set consists of 514 normal, 477 doubtful, 232 mild, 221 moderate, and 206 severe cases of osteoarthritis of the knee. The suggested model for knee osteoarthritis (OA) identification and severity prediction using knee X-ray radiographs has a classification accuracy of more than 95%, with training and validation accuracy of 95% and 87%, respectively
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