We report a case of glomerular disease with both mesangial IgA and subepithelial IgG deposits in the allograft kidney. The patient was a 36 year-old man who had received a renal allograft 1 year previously. Fifteen days before admission, he discovered a microscopic hematuria without clinical evidences of allograft rejection. Light microscopy showed diffuse increase of mesangial matrix without mesangial cell proliferation. Capillary walls were diffusely and mildly thickened. Immunofluorescence microscopy demonstrated both granular deposits of IgA in the mesangium and IgG along the capillary walls. On electron microscopy, electron-dense deposits were identified not only in the mesangium but also on the epithelial side of the glomerular basement membrane.
Adult ; Case Report ; Glomerulonephritis, IGA/*immunology/pathology ; Human ; Immunoglobulin A/*analysis ; Immunoglobulin G/analysis ; Kidney/*immunology/pathology ; Kidney Transplantation/*immunology ; Male ; Transplantation, Homologous

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842–0.969) in the internal test set and 0.870 (95% CI, 0.785–0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%–92.0%) and specificity of 91.3% (95% CI, 79.2%–97.6%) for the internal test set and 78.9% (95% CI, 54.4%–93.9%) and 88.2% (95% CI, 78.7%– 94.4%), respectively, for the external test set. With the model’s assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020–0.168; p = 0.012) and 0.069 (95% CI, 0.002–0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074–0.090; p = 0.850). Conclusion A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.