Abstract
Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from the subjectivity of doctors. In this study, we retrospectively compared five commonly used machine learning methods, especially the CNN network, 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. Furthermore, we present attention maps of CNN to highlight the radiological features affecting the network decision. Such information makes the decision process transparent for practitioners, which builds better trust towards such automatic methods and, moreover, reduces the workload of clinicians, especially for remote areas without enough medical staff.
Highlights
Knee osteoarthritis (OA) is a chronic joint disease characterized by the degeneration, destruction, and bone hyperplasia of articular cartilage
For all of the abovementioned reasons, we believe that clinical evaluation using machine learning methods can significantly improve the diagnosis of knee OA on plain radiographs
The K-Nearest Neighbor Algorithm (KNN), NB, Support Vector Machine (SVM), and RBF classifiers can only figure out whether the X-ray image is grade 0 or grade 1–4 in K-L grade with the highest accuracy of 41.27%
Summary
Knee osteoarthritis (OA) is a chronic joint disease characterized by the degeneration, destruction, and bone hyperplasia of articular cartilage. As a traditional knee OA examination method, plain X-ray images cannot be directly used to evaluate cartilage changes, while its role in the early diagnosis of knee OA is quite limited. X-ray image is still the golden standard for knee OA diagnosis because of its safeness, cost effectiveness, and wide availability. Despite these advantages, X-ray images are not so sensitive when trying to detect early changes in OA. CNN has the ability to quantify learning and classify input information translation through class structure (displacement invariant classification), so it is called “displacement invariant artificial neural network (SIANN)” X-ray images.
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