Background: The number of patients with fragility fracture has been increasing. Although the increasing number of patients with fragility fracture increased the rate of fracture (refracture), the causes of refracture are multifactorial, and its predictors are still not clarified. In this issue, we collected a registry-based longitudinal dataset that contained more than 7000 patients with fragility fractures treated surgically to detect potential predictors for clinical refracture. Methods: Based on the fact that machine learning algorithms are often used for the analysis of a large-scale dataset, we developed automatic prediction models and clarified the relevant features for patients with clinical refracture. Formats of input data containing perioperative clinical information were table data. Clinical refracture was documented as the primary outcome if the diagnosis of fracture was made at postoperative outpatient care. A decision-tree-based model, LightGBM, had moderate accuracy for the prediction in the test and the independent dataset, whereas the other models had poor accuracy or worse. Results: From a clinical perspective, rheumatoid arthritis (RA) and chronic kidney disease (CKD) were noted as the relevant features for patients with clinical refracture, both of which were associated with secondary osteoporosis. Conclusion: The decision-tree-based algorithm showed the precise prediction of clinical refracture, in which RA and CKD were detected as the potential predictors. Understanding these predictors may improve the management of patients with fragility fractures.

Background: Developmental dysplasia of the hip (DDH) is a prevalent issue in infants, with ultrasound crucial for early detection. Existing automatic diagnostic models lack precision due to noise, but 3D technology may enhance it. This study aimed to create and assess a deep-learning-based model for automated DDH diagnosis by using 3D transformation technology on two-dimensional ultrasound images. Methods: A retrospective study of 417 infants at risk of DDH used ultrasound images, combining convolutional neural networks and image processing. The images were analyzed using algorithms such as HigherHRNet-W48. The approach included apex point estimation, signal heterogeneity analysis of ilium, which focused on the bony area with high intensity and evaluate ilium rotation, alpha angle creation, and the establishment of a comprehensive method for DDH diagnosis. Results: Key findings include: (1) Superior accuracy in apex point estimation by the HigherHRNet-W48 model, even better than orthopedic residents. (2) Thorough quality assessments of ultrasound images, leading to qualified and disqualified categories, with qualified images displaying notably lower error rates. (3) The AUC of the model for DDH detection in the qualifying images was 0.92, exceeding the diagnostic accuracy of the resident, indicating the diagnostic capability of the tool. Conclusions: The study developed a deep-learning-based model for DDH detection in infants, melding 3D technology with deep learning to address challenges like noise and rotation in ultrasound images. The study’s innovation demonstrated a comparative accuracy to specialized evaluations, even with non-specialist images, highlighting its potential to assist novice sonographers and enhance diagnostic precision.

Autops Case Rep, vol.13, e2023433, 2023