Objective. To assess the impact of a computerized system on physicians’ accuracy and agreement rate, as compared with unaided diagnosis. Methods. A set of 124 unilateral knee radiographs from the Osteoarthritis Initiative (OAI) study were analyzed by a computerized method with regard to Kellgren-Lawrence (KL) grade, as well as joint space narrowing, osteophytes, and sclerosis Osteoarthritis Research Society International (OARSI) grades. Physicians scored all images, with regard to osteophytes, sclerosis, joint space narrowing OARSI grades and KL grade, in 2 modalities: through a plain radiograph ( unaided) and a radiograph presented together with the report from the computer assisted detection system ( aided). Intraclass correlation between the physicians was calculated for both modalities. Furthermore, physicians’ performance was compared with the grading of the OAI study, and accuracy, sensitivity, and specificity were calculated in both modalities for each of the scored features. Results. Agreement rates for KL grade, sclerosis, and osteophyte OARSI grades, were statistically increased in the aided versus the unaided modality. Readings for joint space narrowing OARSI grade did not show a statistically difference between the 2 modalities. Readers’ accuracy and specificity for KL grade >0, KL >1, sclerosis OARSI grade >0, and osteophyte OARSI grade >0 was significantly increased in the aided modality. Reader sensitivity was high in both modalities. Conclusions. These results show that the use of an automated knee OA software increases consistency between physicians when grading radiographic features of OA. The use of the software also increased accuracy measures as compared with the OAI study, mostly through increases in specificity.
Objective: Joint space width (JSW) has been the gold standard to assess loss of cartilage in knee osteoarthritis (OA). Here we describe a novel quantitative measure of joint space width: standardized JSW (stdJSW). We assess the performance of this quantitative metric for JSW at tracking Osteoarthritis Research Society International (OARSI) joint space narrowing grade (JSN) changes and provide reference values for different JSN grades and their annual change. Methods: We collected 18,934 individual knee images along with JSW and JSN readings from baseline up to month 48 (4 follow-ups) from the OAI study. Standardized JSW and 12-month JSN grade changes were calculated for each knee. For each JSN grade and 12-month grade change, the distribution of JSW loss was calculated for JSW and stdJSW. Area under the ROC curves was calculated on discrimination between different JSN grades for JSW and stdJSW. Standardized response mean (SRM) was used to compare the responsiveness of the two measures to changes in JSN grade. Results: The areas under the receiver operating characteristic (ROC) curve (AUC) for stdJSW at discriminating between successive JSN grades were AUC stdJSW ¼ 0.87, 0.95, and 0.96, for JSN>0, JSN>1 and JSN>2, respectively, whereas these were AUC fJSW ¼ 0.79, 0.90, 0.98 for absolute JSW. We find that standardized JSW is significantly more responsive than absolute JSW, as measured by the SRM. Conclusions: Our results show that stdJSW outperforms absolute JSW at discriminating and tracking changes in JSN and further that this effect is in part because stdJSW cancels JSW variations attributed to patient height variations.
Purpose The aims of this study were to (1) analyze the impact of an artificial intelligence (AI)-based computer system on the accuracy and agreement rate of board-certified orthopaedic surgeons (= senior readers) to detect X-ray features indicative of knee OA in comparison to unaided assessment and (2) compare the results to those of senior residents (= junior readers). Methods One hundred and twenty-four unilateral knee X-rays from the OAI study were analyzed regarding Kellgren–Lawrence grade, joint space narrowing (JSN), sclerosis and osteophyte OARSI grade by computerized methods. Images were rated for these parameters by three senior readers using two modalities: plain X-ray (unaided) and X-ray presented alongside reports from a computer-assisted detection system (aided). After exclusion of nine images with incomplete annotation, intraclass correlations between readers were calculated for both modalities among 115 images, and reader performance was compared to ground truth (OAI consensus). Accuracy, sensitivity and specificity were also calculated and the results were compared to those from a previous study on junior readers. Results With the aided modality, senior reader agreement rates for KL grade (2.0-fold), sclerosis (1.42-fold), JSN (1.37-fold) and osteophyte OARSI grades (3.33-fold) improved significantly. Reader specificity and accuracy increased significantly for all features when using the aided modality compared to the gold standard. On the other hand, sensitivity only increased for OA diagnosis, whereas it decreased (without statistical significance) for all other features. With aided analysis, senior readers reached similar agreement and accuracy rates as junior readers, with both surpassing AI performance. Conclusion The introduction of AI-based computer-aided assessment systems can increase the agreement rate and overall accuracy for knee OA diagnosis among board-certified orthopaedic surgeons. Thus, use of this software may improve the standard of care for knee OA detection and diagnosis in the future. Level of evidence Level II.
tissues in OA. In the lateral femoral condyle of the TKR patients, Raman analysis depicted mineral of greater crystallinity (i.e. a measure of the crystal size and/or perfection), in CC and SBP. This was supported by observed thicker mineral crystals in mSAXS analysis. Degree of mineralization decreases especially during early OA as seen in the lateral compartment. This could be related to the early-stage inflammation or changes in the remodeling activity of OC tissues. In summary, contradicting our initial hypothesis, the mineral composition of the less-loaded lateral compartment seems to be more affected by OA than the main load-bearing medial compartment.
Introduction: Loss of cartilage is one of the hallmark radiographic symptoms of osteoarthritis and the direct cause of much of the disability directly related to OA. The rate of cartilage loss can range from a slow deterioration process, lasting decades, to a very rapid deterioration leading to complete loss in as little as 24 months 1. In fact, evidence has been gathering that a subset of OA patients develops an “accelerated” form of knee osteoarthritis 2. Hypotheses: The rate of cartilage loss can vary widely between patients at risk of or suffering from knee osteoarthritis (OA) but its causes remain unknown. We investigate prediction of future joint space width (JSW) loss from single time point quantitative and semi-quantitative radiographic features. Methods: Bilateral knee radiographs acquired at several time points in the context of the MOST study from 2651 patients (1079 female, 1572 male) were collected. Joint space narrowing (JSN), osteophyte and sclerosis OARSI grades, as well as Kellgren-Lawrence (KL) grade and joint space width were obtained from each image using an automated software algorithm. Individuals were classified as fast progressors if the rate of JSW loss, measured via linear regression, was above 10% baseline JSW. Fast progressors were predicted using a logistic regression model trained with KL and OARSI grades at baseline as independent variables. Independent validation was performed on 1900 individuals (1079 female, 821 male) from the Osteoarthritis Initiative (OAI) study. Performance was characterized by the area under the ROC curve (ROC-AUC). Confidence intervals were calculated by bootstrapping. Results: AUCs of 0.84 (0.82; 0.87) were achieved for classifying individual knees as fast progressors on the validation dataset (OAI). KL and sclerosis OARSI grades were the main predictors of rapid cartilage loss. Conclusion: We demonstrate prediction of future rapid cartilage loss from a single plain radiograph with validation on an independent dataset. Sclerosis OARSI grade, but not osteophytes OARSI grade, was a predictor of rapid cartilage loss, suggesting a non-canonical mode of OA progression.
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