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Background: We aimed to use open source data to understand the incidence, trends, and regional differences of finger and partial hand amputations on a national level in individuals aged 15 or older in Sweden., Methods: We analyzed 6,918 patients aged 15 and older who had experienced finger and partial hand amputations. Incidence rates, trends, and regional disparities were assessed using negative binomial regression models and Student's t-tests. Future trend prediction was performed using Poisson regression., Results: Finger amputations declined most, followed by partial hand and thumb amputations. Regional variations existed, with Stockholm having the lowest and Gotland highest incidence respectively. Overall, the incidence of finger, thumb and partial hand amputations in Sweden decreased slightly. Future trend analysis indicated decreasing incidence., Conclusion: Although, lacking in definition, publicly available data can be used for monitoring of finger, thumb, and partial hand amputation incidence on a national level. Sex, age, and regional differences were observed, suggesting the need for targeted interventions to address disparities and mitigate the burden of finger and partial hand amputations on affected individuals., (© 2024. The Author(s).)

Introduction: Orthopedic injuries to the foot constitute a significant portion of lower extremity injuries, necessitating an understanding of trends for effective preventive strategies and resource allocation. Demographic shifts, improved traffic safety, and lifelong physical activity may alter incidence rates, trauma mechanisms, and fracture distribution. This study explores the prevalence of foot fractures in Sweden using publicly available data., Methods: Utilizing data from the Swedish National Board of Health and Welfare (SNBHW) spanning 2008-2022, retrospective study focuses on foot fractures in Sweden. Analysis includes calculating annual incidence rates per 100,000 person-years, assessing temporal trends, and exploring seasonal variations. Poisson regression analysis was used for projections into 2035., Results: Between 2008-2022, the average annual foot fracture incidence was 11,942, with notable fluctuations influenced by the COVID-19 pandemic. Age and sex disparities impact rates, and seasonal variance highlights increased incidence in summer. By 2035, foot fractures will decreasae amongst several demographic groups., Conclusion: This study provides insights into temporal trends, sex differences, and seasonal variations foot fracture patterns in Sweden. The identified trends suggest the utilization of targeted preventive strategies, efficient resource allocation, and informed healthcare planning. Despite limitations, this research offers valuable insights into foot fractures within the Swedish population, utilizing publicly aggregated data., (© 2024. The Author(s).)

Background: Hip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity., Methods: We collected pelvic radiographs of 571 patients from two single-center cohorts and one multicenter cohort. The radiographs were split in half to create hip radiographs (n = 1022). One orthopaedic surgeon and one resident assessed the radiographs for hip dysplasia on either side. We used the center edge (CE) angle as the primary diagnostic criteria. Hips with a CE angle < 20°, 20° to 25°, and > 25° were labeled as dysplastic, borderline, and normal, respectively. The dysplastic hips were also classified with both Crowe and Hartofilakidis classification of dysplasia. The dataset was divided into train, validation, and test subsets using 80:10:10 split-ratio that were used to train two deep learning models to classify images into normal, borderline and (1) Crowe grade 1-4 or (2) Hartofilakidis grade 1-3. A pre-trained on Imagenet VGG16 convolutional neural network (CNN) was utilized by performing layer-wise fine-turning., Results: Both models struggled with distinguishing between normal and borderline hips. However, achieved high accuracy (Model 1: 92.2% and Model 2: 83.3%) in distinguishing between normal/borderline vs. dysplastic hips. The overall accuracy of Model 1 was 68% and for Model 2 73.5%. Most misclassifications for the Crowe and Hartofilakidis classifications were +/- 1 class from the correct class., Conclusions: This pilot study shows promising results that a deep learning model distinguish between normal and dysplastic hips with high accuracy. Future research and external validation are warranted regarding the ability of deep learning models to perform complex tasks such as identifying and classifying disorders using plain radiographs., Level of Evidence: Diagnostic level IV., (© 2024. The Author(s).)

Background: Forearm and olecranon fractures are a common orthopaedic injury. This study aimed to analyse whether the incidence of forearm injury is changing and identifying trends in the number of forearm and olecranon fractures using public aggregated data in Sweden., Methods: The number of forearm and olecranon fractures as defined by the number of registered diagnoses with the ICD-10 code of S52 were collected and normalized per 100,000 inhabitants and stratified per sex, age, and month. Age-adjusted incidence for forearm and olecranon fractures were calculated using the direct method. Poisson regression was used to analyse monthly, seasonal and yearly change in forearm and olecranon fracture incidence. Logistical regression was used to predict future trends of forearm and olecranon fractures., Results: The findings revealed a slight decreasing trend in forearm and olecranon fractures. The average incidence rate during the study period was 333 with women having a higher incidence rate than men. More fractures occurred in the winter months. Fluctuations in the number of forearm and olecranon fractures were observed during 2020 which may be influenced by the COVID-19 pandemic. Based on current data, forearm and olecranon fractures are expected to decrease in Sweden by 2035., Conclusion: This study describes the trend of forearm and olecranon fractures among individuals according to sex and age in Sweden using easily obtainable data. Trends in forearm and olecranon fractures are dependent on sex and age but generally show a decreasing trend. More precise studies are needed in order to properly quantify the specific incidence of various subtypes of forearm and olecranon fractures and associated risk factors., (© 2024. The Author(s).)