Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present 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. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average multiclass accuracy of 66.71% compared to the annotations given by a committee of clinical experts. Here, we also report a radiological OA diagnosis area under the ROC curve of 0.93. Besides this, we present attention maps highlighting the radiological features affecting the network decision. Such information makes the decision process transparent for the practitioner, which builds better trust toward automatic methods. We believe that our model is useful for clinical decision making and for OA research; therefore, we openly release our training codes and the data set created in this study. Osteoarthritis (OA) is the most common musculoskeletal disorder in the world. The literature shows that hip and knee OA are the eleventh highest global disability factor 1 , thereby, causing a large economical burden to the society. It has been reported that the estimated overall costs per patient for OA treatments reach 19,000 €/year 2 . Part of these costs arise from the current clinical inability to systematically diagnose the disease at an early stage, when it might still be possible to slow down its progression or at least reduce the impact of its future disability. Because there is no effective cure for OA besides total joint replacement surgery at the advanced stage, an early diagnosis and behavioural interventions 3 remain the only available options to prolong the patients' healthy years of life. Clinically, early diagnosis of OA is possible; however, currently, it requires the use of expensive magnetic resonance imaging (MRI) available only at specialised centres or in private practice. Moreover, this modality does not capture the changes in the bone architecture, which might indicate the earliest OA progression 4 . The current gold standard for diagnosing OA, besides the always required routine clinical examination of the symptomatic joint, is X-ray imaging (plain radiography), which is safe, cost-efficient and widely available. Despite these advantages, it is well known that plain radiography is insensitive when attempting to detect early OA changes. This can be explained by several facts: first, a hallmark of OA and the best measure of its progression is the degeneration and wear of the articular cartilage -a tissue that cannot be directly seen in plain radiography; second, although the evaluation of the changes in the joint should be a three-dimensional (3D) problem, the imaging modality uses only two-dimensional (2D) sum projection; and finally, the interpretation of the resulting image requires a significantly experienced practitioner. Eventually, the cartilage degeneration and wear are indirectly estimated by the assessment of joint-space narrowing and bony changes, that is, osteophytes and subchondral sclerosis 5 . For these reasons, an early OA diagnosis is difficult in clinical practice. Apart from the aforementioned limitations of plain radiography, OA diagnosis is also highly dependent on the subjectivity of the practitioner due to the absence of a precisely defined grading system. The commonly used
Tiulpin et al. REF also used CNN to detect knee OA with X-ray images from 3000 subjects.
Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach
Scientific Reports
Scientific Reports
Computer Science Medicine