No abstract

Background: Tibial eminence fractures are bony avulsions of the anterior cruciate ligament from its insertion on the intercondylar eminence. Numerous anatomic factors have been associated with anterior cruciate ligament injuries, such as posterior tibial slope, but there are few studies evaluating the association with tibial eminence fracture. Purpose: To compare posterior tibial slope of pediatric patients with and without tibial eminence fractures. We hypothesized that a steeper posterior tibial slope would be associated with tibial eminence fracture. Study Design: Cohort study; Level of evidence, 3. Methods: Patients who underwent surgical treatment of tibial eminence fracture were retrospectively identified between January 2000 and July 2021. Adults aged >20 years and those without adequate imaging were excluded. Controls without gross ligamentous or osseous pathology were identified. Descriptive information and Meyers and McKeever classification were recorded. Posterior tibial slope measurements were obtained by 2 independent orthopaedic surgeons twice, with measurements separated by 3 weeks. Chi-square tests and independent-samples t tests were used to compare posterior tibial slope and patient characteristics. Inter- and intrareviewer variability was determined via the intraclass correlation coefficient. Results: A total of 51 patients with tibial eminence fractures and 57 controls were included. By sex, tibial eminence fractures occurred among 34 male and 17 female patients with a mean age of 10.9 years. The posterior tibial slope among those with tibial eminence fractures (9.7°) was not significantly greater than that of controls (8.8°; P = .07). Male patients with a tibial eminence fracture had significantly steeper slopes compared with controls (10.0° vs 8.4°; P = .006); this difference was not observed between female patients and female controls. Patients with a slope ≥1 SD above the mean (12.0°) had 3.8 times greater odds (95% CI, 1.3-11.6; P = .017) of having a tibial eminence fracture. Male patients with a posterior tibial slope >12° had 5.8 times greater odds (95% CI, 1.1-29.1; P = .034) of having a tibial eminence fracture compared with male controls. Conclusion: Male patients undergoing surgical fixation of a tibial eminence fracture had an increased posterior tibial slope as compared with case-controls. Increased posterior tibial slope may be a risk factor for sustaining a tibial eminence fracture, although the clinical significance of this deserves further investigation.

To develop machine learning methods to quantify joint damage in patients with rheumatoid arthritis (RA), we developed the RA2 DREAM Challenge, a crowdsourced competition that utilized existing radiographic images and "gold-standard" scores on 674 sets of films from 562 patients. Training and leaderboard sets were provided to participants to develop methods to quantify joint space narrowing and erosions. In the final round, participants submitted containerized codes on a test set; algorithms were evaluated using weighted root mean square error (RMSE). In the leaderboard round, there were 173 submissions from 26 teams in 7 countries. Of the 13 submissions in the final round, four top-performing teams were identified. Robustness of results was assessed using Bayes factor and validated using an independent set of radiographs. The top-performing algorithms, which consisted of different styles of deep learning models, provided accurate and robust quantification of joint damage in RA. Ultimately, these methods lay the groundwork to accelerate research and help clinicians to optimize treatments to minimize joint damage.

IMPORTANCE An automated, accurate method is needed for unbiased assessment quantifying accrual of joint space narrowing and erosions on radiographic images of the hands and wrists, and feet for clinical trials, monitoring of joint damage over time, assisting rheumatologists with treatment decisions. Such a method has the potential to be directly integrated into electronic health records. OBJECTIVESTo design and implement an international crowdsourcing competition to catalyze the development of machine learning methods to quantify radiographic damage in rheumatoid arthritis (RA). DESIGN, SETTING, AND PARTICIPANTSThis diagnostic/prognostic study describes the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM Challenge), which used existing radiographic images and expert-curated Sharp-van der Heijde (SvH) scores from 2 clinical studies (674 radiographic sets from 562 patients) for training (367 sets), leaderboard (119 sets), and final evaluation (188 sets). Challenge participants were tasked with developing methods to automatically quantify overall damage (subchallenge 1), joint space narrowing (subchallenge 2), and erosions (subchallenge 3). The challenge was finished on June 30, 2020. MAIN OUTCOMES AND MEASURESScores derived from submitted algorithms were compared with the expert-curated SvH scores, and a baseline model was created for benchmark comparison.Performances were ranked using weighted root mean square error (RMSE). The performance and reproductivity of each algorithm was assessed using Bayes factor from bootstrapped data, and further evaluated with a postchallenge independent validation data set. RESULTSThe RA2-DREAM Challenge received a total of 173 submissions from 26 participants or teams in 7 countries for the leaderboard round, and 13 submissions were included in the final evaluation. The weighted RMSEs metric showed that the winning algorithms produced scores that were very close to the expert-curated SvH scores. Top teams included Team Shirin for subchallenge 1 (weighted RMSE, 0.44), HYL-YFG (Hongyang Li and Yuanfang Guan) subchallenge 2 (weighted RMSE, 0.38), and Gold Therapy for subchallenge 3 (weighted RMSE, 0.43). Bootstrapping/Bayes factor approach and the postchallenge independent validation confirmed the reproducibility and the estimation concordance indices between final evaluation and postchallenge independent validation data set were 0.71 for subchallenge 1, 0.78 for subchallenge 2, and 0.82 for subchallenge 3. CONCLUSIONS AND RELEVANCEThe RA2-DREAM Challenge resulted in the development of algorithms that provide feasible, quick, and accurate methods to quantify joint damage in RA. (continued) Key Points Question Can a worldwide collaborative effort develop machine learning algorithms to quantify joint space narrowing and erosions automatically to improve the current visual inspection approach to radiography in rheumatoid arthritis (RA)? Findings This prognostic study assesses an international, crowdsourcing competition using scored radiographs of...

No abstract