Background: Studies investigating constitutional alignment across various grades of osteoarthritis (OA) are limited. This study explored the distribution of Coronal Plane Alignment of the Knee (CPAK) types and associated radiographic parameters with increasing OA severity. Methods: In this retrospective cross-sectional study, 17,365 knees were analyzed using deep learning software for radiographic measurements. Knees were categorized on the basis of the Kellgren and Lawrence (KL) grade and CPAK type. Radiographic measurements were the hip-knee-ankle angle (HKAA), lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), arithmetic HKAA (aHKA), joint line obliquity (JLO), and joint line convergence angle (JLCA). Age-stratified analysis was performed to differentiate the impact of age on OA severity. Results: A shift in the most common CPAK type from II to I was found with increasing KL grade (p < 0.05). Furthermore, there was a corresponding increase in LDFA and JLCA with increasing KL grade, while HKAA, MPTA, and aHKA decreased after KL grade 2. Age exhibited limited association with LDFA and MPTA, suggesting that OA severity is the dominant factor related to the CPAK distribution. Conclusions: The study found a shift in CPAK type with worsening OA. It is possible that constitutional varus types are more susceptible to OA, or that their increased OA prevalence is related to anatomical changes. This analysis offers new insights into alterations in CPAK type that occur with OA and underscores the importance of understanding pre-arthritic anatomy when performing joint reconstruction. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Aims: The aim of this study was to develop and evaluate a deep learning-based model for classification of hip fractures to enhance diagnostic accuracy.Methods: A retrospective study used 5,168 hip anteroposterior radiographs, with 4,493 radiographs from two institutes (internal dataset) for training and 675 radiographs from another institute for validation. A convolutional neural network (CNN)-based classification model was trained on four types of hip fractures (Displaced, Valgus-impacted, Stable, and Unstable), using DAMO-YOLO for data processing and augmentation. The model’s accuracy, sensitivity, specificity, Intersection over Union (IoU), and Dice coefficient were evaluated. Orthopaedic surgeons’ diagnoses served as the reference standard, with comparisons made before and after artificial intelligence assistance.Results: The accuracy, sensitivity, specificity, IoU, and Dice coefficients of the model for the four fracture categories in the internal dataset were as follows: Displaced (1.0, 0.79, 1.0, 0.70, 0.82), Valgus-impacted (1.0, 0.80, 1.0, 0.70, 0.82), Stable (0.99, 0.95, 0.99, 0.83, 0.89), and Unstable (1.0, 0.98, 0.99, 0.86, 0.92), respectively. For the external validation dataset, the sensitivity and specificity were as follows: Displaced (0.83, 0.94), Valgus-impacted (0.89, 0.90), Stable (0.88, 0.95), and Unstable (0.85, 0.99), respectively. The overall means (Micro AVG and Macro AVG) for the external dataset were Micro AVG (0.83 (SD 0.05), 0.96 (SD 0.01)) and Macro AVG (0.69 (SD 0.02), 0.95 (SD 0.02)), respectively.Conclusion: Compared to human diagnosis alone, our study demonstrates that the developed model significantly improves the accuracy of detecting and classifying hip fractures. Our model has shown great potential in assisting clinicians with the accurate diagnosis and classification of hip fractures.Cite this article: Bone Joint J 2025;107-B(2):213–220.

Kim SE, et al. Clin Orthop Surg. 2024 Mar;16:e13. https://doi.org/10.4055/cios23179

Introduction: Population-based differences in knee alignment patterns may impact osteoarthritis (OA) progression. This study compares lower extremity alignment in knee OA between Middle Eastern (UAE) and East Asian (South Korean) populations using artificial intelligence (AI)-assisted analysis. Methods: A retrospective review included patients with knee symptoms from South Korea (2009–2019) and the United Arab Emirates (UAE) (2015–2024). Exclusion criteria comprised prior knee surgeries, significant bony attrition, and low-quality radiographs. Propensity score matching controlled for age and sex differences between populations. Alignment parameters (hip–knee–ankle angle (HKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), and joint line convergence angle (JLCA)) and OA severity (Kellgren–Lawrence (KL) grade) were measured using artificial intelligence (AI) software, along with the analysis of coronal plane alignment of the knee (CPAK) classification. Subanalyses by sex and age group (under 40, 40–60, and over 60 years) were also conducted. Results: A total of 1098 UAE and 7138 South Korean patients (2196 and 14,276 knees, respectively) were evaluated in this study. Post-matching (1:2), body mass index was significantly higher in UAE patients (p < 0.001). CPAK type 1 was predominant in UAE male patients (42.4%), whereas type 2 was more common in South Korean male patients (30.6%). Female patients in both populations predominantly exhibited CPAK type 2 (UAE 30.6%; South Korea 35.3%). UAE patients showed a lower MPTA with increasing age, indicating a trend toward more varus alignment in older individuals. Conclusions: A propensity score-matched analysis revealed significant alignment differences between Middle Eastern and East Asian populations, underscoring the importance of population-specific considerations in OA management.