Purpose: Limited published data regarding the ROSA (Robotic Surgical Assistant) learning curve exist. This study evaluated the number of cases needed for an expert orthopaedic surgeon to master the ROSA system and match the operative time of robotic (raTKAs) and manual primary total knee arthroplasties (mTKAs). Methods: This retrospective comparative cohort study included two hundred patients with primary knee osteoarthritis. The study group consisted of an expert surgeon’s first 100 raTKAs. The control group included 100 patients that underwent mTKAs from the same surgeon during the same period. The consecutive cases in each group were divided into ten subgroups, each of 10 cases. The groups were comparable concerning age, sex, BMI and Kellgren–Lawrence classification. We compared each subgroup's operative time and complications in mTKA and raTKA groups. We performed a cumsum analysis to construct the ROSA learning curve. Results: The first non-significant difference between the mTKAs and raTKAs operative times was observed in the subgroup of 62 to 71 cases. Till then, the operative time has been significantly lower for the mTKA than the raTKA group. The following groups of tens analysis (8th, 9th and 10th) showed no operative time difference between groups. The learning curve analysis demonstrated that the surgeon switched to the mastering phase from case 73 onwards. The two groups had no complication rate differences. Conclusion: Our study demonstrated that about 70 cases are necessary for a senior surgeon to balance operative time between mTKAs and raTKAs using the ROSA system.

Developmental dysplasia of the hip (DDH) is a common musculoskeletal disorder in infancy, and early detection is essential for optimal clinical outcomes. Radiographic assessment is traditionally based on angular measurements, which may be limited by variability in landmark identification and do not fully capture the complex morphology of the hip joint. In this study, we investigate whether geometric features derived from the hip joint articulation space can be used to differentiate between normal and dysplastic hips in infant radiographs. Pelvic X-ray images from infants (mean age 4.5 ± 0.83 months) were analyzed, and custom segmentation masks were developed to isolate the joint space region. A total of 99 geometric and radiomic features were extracted and evaluated using statistical analysis and supervised machine learning methods. Multiple features demonstrated strong discriminative power between normal and DDH (p < 0.001), with shape and spatial distribution characteristics showing the highest relevance. Classification models achieved an F1-score of approximately 80% on the full dataset. Notably, patient age was identified as a significant confounding factor, and analysis on an age-matched subset improved classification performance to 94% accuracy and 93% recall. These findings suggest that geometric characterization of the hip joint space provides a promising and interpretable framework for DDH detection. The results also highlight the importance of age-stratified analysis in pediatric imaging. Further validation on larger and more diverse datasets is required to assess clinical applicability.