Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilises raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78–0.81) and Average Precision (AP) of 0.68 (0.66–0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74–0.77) and AP of 0.62 (0.60–0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalised therapeutic plans.

Imaging in osteoarthritis has developed rapidly in the last decade and, despite the lack of effective therapies, there is hope that imaging might help drive a therapeutic breakthrough in the near f... Osteoarthritis (OA) is a highly prevalent chronic condition with marked implications for affected individuals and public health care. There are available treatments to manage pain and symptoms but ...

Knee osteoarthritis (OA) is the most common musculoskeletal disease in the world. In primary healthcare, knee OA is diagnosed using clinical examination and radiographic assessment. Osteoarthritis Research Society International (OARSI) atlas of OA radiographic features allows performing independent assessment of knee osteophytes, joint space narrowing and other knee features. This provides a fine-grained OA severity assessment of the knee, compared to the gold standard and most commonly used Kellgren–Lawrence (KL) composite score. In this study, we developed an automatic method to predict KL and OARSI grades from knee radiographs. Our method is based on Deep Learning and leverages an ensemble of residual networks with 50 layers. We used transfer learning from ImageNet with a fine-tuning on the Osteoarthritis Initiative (OAI) dataset. An independent testing of our model was performed on the Multicenter Osteoarthritis Study (MOST) dataset. Our method yielded Cohen’s kappa coefficients of 0.82 for KL-grade and 0.79, 0.84, 0.94, 0.83, 0.84 and 0.90 for femoral osteophytes, tibial osteophytes and joint space narrowing for lateral and medial compartments, respectively. Furthermore, our method yielded area under the ROC curve of 0.98 and average precision of 0.98 for detecting the presence of radiographic OA, which is better than the current state-of-the-art.

To examine the validity of power Doppler ultrasound imaging to identify synovitis, using histopathology as gold standard, and to assess the performance of ultrasound equipments. 44 synovial sites in small and large joints, bursae and tendon sheaths were depicted with ultrasound. A synovial biopsy was performed on the site depicted and a synovial sample was taken for histopathological evaluation. The performance of three ultrasound devices was tested using flow phantoms. A positive Doppler signal was detected in 29 of 35 (83%) of the patients with active histological inflammation. In eight additional samples, histological examination showed other pathological synovial findings and a Doppler signal was detected in five of them. No significant correlation was found between the amount of Doppler signal and histological synovitis score (r = 0.239, p = NS). The amount of subsynovial infiltration of polymorphonuclear leucocytes and surface fibrin correlated significantly with the amount of power Doppler signal: r = 0.397 (p<0.01) and 0.328 (p<0.05), respectively. The ultrasound devices differed in showing the smallest detectable flow. A negative Doppler signal does not exclude the possibility of synovitis. A positive Doppler signal in the synovium is an indicator of an active synovial inflammation in patients. A Doppler signal does not correlate with the extent of the inflammation and it can also be seen in other synovial reactions. It is important that the quality measurements of ultrasound devices are reported, because the results should be evaluated against the quality of the device used.