Hemobilia, in patients with the diagnosis of polyarteritis nodosa, is rare at clinical presentation and has a grave prognosis. We describe a case of massive hemobilia, due to aneurysmal rupture, in a patient with polyarteritis nodosa. A 39-year-old man was admitted to the hospital with upper abdominal pain. The patient had a history of partial small bowel resection, for intestinal infarction, about 5 years prior to this presentation. Abdominal computed tomography demonstrated multiple high attenuation areas in the bile duct and gallbladder. Hemobilia with blood seepage was visualized on endoscopic retrograde cholangiopancreatography; this bleeding stopped spontaneously. The following day, the patient developed a massive gastrointestinal bleed with resultant hypovolemic shock. Emergent hepatic angiogram revealed multiple microaneurysms; a communication was identified between a branch of the left hepatic artery and the bile duct. Hepatic arterial embolization was successfully performed. The underlying disease, polyarteritis nodosa, was managed with prednisolone and cyclophosphamide.
Rupture/*complications ; Polyarteritis Nodosa/*physiopathology ; Male ; Humans ; Hepatic Artery/*pathology ; Hemobilia/diagnosis/*etiology ; *Embolization, Therapeutic ; Aneurysm, Ruptured/*complications/therapy ; Adult

Purpose: To evaluate the applicability of Greulich-Pyle (GP) standards to bone age (BA) assessment in healthy Korean children using manual and deep learning-based methods. Materials and Methods: We collected 485 hand radiographs of healthy children aged 2–17 years (262 boys) between 2008 and 2017. Based on GP method, BA was assessed manually by two radiologists and automatically by two deep learning-based BA assessment (DLBAA), which estimated GP-assigned (original model) and optimal (modified model) BAs. Estimated BA was compared to chronological age (CA) using intraclass correlation (ICC), Bland-Altman analysis, linear regression, mean absolute error, and root mean square error. The proportion of children showing a difference >12 months between the estimated BA and CA was calculated. Results: CA and all estimated BA showed excellent agreement (ICC ≥0.978, p<0.001) and significant positive linear correlations (R2 ≥0.935, p<0.001). The estimated BA of all methods showed systematic bias and tended to be lower than CA in younger patients, and higher than CA in older patients (regression slopes ≤-0.11, p<0.001). The mean absolute error of radiologist 1, radiologist 2, original, and modified DLBAA models were 13.09, 13.12, 11.52, and 11.31 months, respectively. The difference between estimated BA and CA was >12 months in 44.3%, 44.5%, 39.2%, and 36.1% for radiologist 1, radiologist 2, original, and modified DLBAA models, respectively. Conclusion Contemporary healthy Korean children showed different rates of skeletal development than GP standard-BA, and systemic bias should be considered when determining children’s skeletal maturation.

Background: Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data. Methods: This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models. Results: Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women. Conclusion DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.