# Knee Osteoarthritis Classification Using 3D CNN and MRI
## 1 Introduction  
The most common form of joint disorder in the United States is osteoarthritis (OA) [1]. Knee OA can cause pain and is the number one disease at causing loss of ability to perform daily activities such as walking and stair climbing [2]. Knee OA is associated with age [3] and is characterized by the loss of articular cartilage volume [4]. OA is viewed as a \"whole-organ\" disorder, manifesting damage to a range of articular structures, especially the hyaline cartilage, meniscus, periarticular bone, ligaments, and tendons [5]. Despite its importance for public health, we have no interventions that effectively modify the OA disease process [6]. The absence of useful biomarkers to detect OA progression is a major technological obstacle to the development of treatment and prevention of knee OA [7]. While joint replacement is effective for treating end-stage OA, the evaluation of potential disease-modifying treatments in populations meeting current clinical criteria for OA has had limited success [6]. In the past decade, early diagnosis and early treatment strategies in rheumatoid arthritis have reduced patient morbidity and associated costs [8]. The early diagnosis and treatment of OA conditions may similarly improve outcomes and reduce disability and costs for OA. However, the absence of useful image biomarkersto detect OA progression has been a critical technology gap in the early diagnosis and treatment of OA [9]. The conventional radiographs (X-rays) are commonly used for routine knee OA examinations. An X-ray of a joint with osteoarthritis typically shows a narrowing of the space between the bones of the knee joint where the cartilage has worn away. However, symptoms of knee OA may arise before the damage can be seen in standard X-rays. For example, [PERSON] et al. [10] described how X-rays are unable to show certain structural phenotypes of OA and cannot detect some detrimental findings which can indicate risk of disease that would progress rapidly. The advent of magnetic resonance imaging (MRI) offers the promise of addressing the critical technology gap by allowing quantification of structural damage in joints. For this reason, radiologists at hospitals often use the more sensitive magnetic resonance imaging (MRI) for OA early detection. [PERSON] et al. [11] pointed out that OA needs early detection, and MRI is a noninvasive way for detecting early biomarkers. To promote the evaluation of OA MRI biomarkers, the National Institutes of Health (NIH) launched the Osteoarthritis Initiative (OAI) cohort study, which includes four clinical centers that recruited approximately 4800 men and women (ages 45-79 years) with or at risk for knee OA. The OAI collected a wealth of data on its participants over an eight-year span. The study included annual knee MRIs for the first four years and then biannual knee MRIs for the subsequent 4 years [12]. One of the goals of creating the OAI dataset was to discover the objective, measurable standards of disease diagnosis and progression, and to determine the predictive role of MRI changes for subsequent radiographic and clinical changes related to the development of knee OA. The 3-dimensional (3D) MR images allow for both viewing the knee as a \"whole organ\" and depicting all of the tissues of the joint [13]. While cartilage degradation and other biomarkers can be manually detected, it is time-consuming to process the volume of 3D MR images. Thus, there is a need to automate these processes with machine learning techniques. Convolutional neural networks (CNNs) are a class of deep learning techniques that are designed to work with images and can remove the need for handcrafted feature extractors [14]. CNNs have been used for various image classification tasks, with recent studies developing CNN models for medical image analysis. The early work of using CNNs to classify knee OA was mainly applied to radiographic (X-ray) images [15; 16; 17; 18]. [PERSON] et al.