Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

@article{Antony2019FeatureLT,
  title={Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity},
  author={Joseph Antony and Kevin McGuinness and Kieran Moran and Noel E. O'Connor},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.08840}
}
This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the associated clinical and diagnostic features of knee OA from X-ray images. Also, this chapter demonstrates that feature learning in a supervised manner is more effective than using conventional handcrafted features for automatic detection of knee joints and fine-grained knee OA image classification. In the general… 
Multicentre Study Using Machine Learning Methods in Clinical Diagnosis of Knee Osteoarthritis
TLDR
Five commonly used machine learning methods are compared to predict the real-world X-ray imaging data of knee joints from two different hospitals using Kellgren-Lawrence (K-L) grade of knee OA to help doctors choose proper auxiliary tools.

References

SHOWING 1-10 OF 93 REFERENCES
Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks
TLDR
It is argued that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy, which leads to the formulation of the prediction of KL grades as a regression problem and improves accuracy.
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity Using Convolutional Neural Networks
TLDR
A new approach to automatically detect the knee joints using a fully convolutional neural network (FCN) to automatically quantify the severity of knee OA using X-ray images, with extremely promising results that outperform existing approaches.
A Novel Method for Automatic Localization of Joint Area on Knee Plain Radiographs
TLDR
A novel and computationally efficient method to automatically annotate conventional knee radiographs within 14–16 ms and high resolution ones within 170 ms is developed and demonstrated that the developed method is suitable for large-scale analyses.
Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach
TLDR
A new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale is presented.
Knee X-Ray Image Analysis Method for Automated Detection of Osteoarthritis
TLDR
Experimental results show that moderate OA and minimal OA can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively.
Effective Feature Extraction Based Automatic Knee Osteoarthritis Detection and Classification using Neural Network
TLDR
A new automatic knee OA detection system based on feature extraction and artificial neural network is developed, which results 98.5% of classification accuracy at training stage and 92% at testing stage.
Automated Shape and Texture Analysis for Detection of Osteoarthritis from Radiographs of the Knee
TLDR
This work presents a fully automated method to standardise the measurement of OA features in the knee used to diagnose disease grade and demonstrates that Random Forests trained on simple pixel ratio features are as effective as the best previously reported texture measures on this task.
Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
TLDR
A novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively, which performs better than a state-of-the-art method using 3D multi-scale features.
Analysis of MRI for knee osteoarthritis using machine learning
TLDR
There is a strong correlation between traditional morphological measures of the articular cartilage and the biomarkers identified using the manifold learning algorithm that is proposed, and a combination of these markers is proposed to yield a diagnostic imaging biomarker with superior performance.
...
1
2
3
4
5
...