Automatic detection and classification of knee osteoarthritis using deep learning approach.

PURPOSE: We developed a tool for locating and grading knee osteoarthritis (OA) from digital X-ray images and illustrate the possibility of deep learning techniques to predict knee OA as per the Kellgren-Lawrence (KL) grading system. The purpose of the project is to see how effectively an artificial intelligence (AI)-based deep learning approach can locate and diagnose the severity of knee OA in digital X-ray images.
METHODS: Selection criteria: Patients above 50 years old with OA symptoms (knee joint pain, stiffness, crepitus, and functional limitations) were included in the study. Medical experts excluded patients with post-surgical evaluation, trauma, and infection from the study. We used 3172 Anterior-posterior view knee joint digital X-ray images. We have trained the Faster RCNN architecture to locate the knee joint space width (JSW) region in digital X-ray images and we incorporate ResNet-50 with transfer learning to extract the features. We have used another pre-trained network (AlexNet with transfer learning) for the classification of knee OA severity. We trained the region proposal network (RPN) using manual extract knee area as the ground truth image and the medical experts graded the knee joint digital X-ray images based on the Kellgren-Lawrence score. An X-ray image is an input for the final model, and the output is a Kellgren-Lawrence grading value.
RESULTS: The proposed model identified the minimal knee JSW area with a maximum accuracy of 98.516%, and the overall knee OA severity classification accuracy was 98.90%.
CONCLUSIONS: Today numerous diagnostic methods are available, but tools are not transparent and automated analysis of OA remains a problem. The performance of the proposed model increases while fine-tuning the network and it is higher than the existing works. We will extend this work to grade OA in MRI data in the future.