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Aim: To evaluate endothelial dysfunction and subclinical atherosclerosis in Behcet’s disease (BD) by measuring the common carotid artery (CCA) wall stiffness and carotid intima-media thickness (CIMT).Materials and methods: We prospectively evaluated CIMT and the CCA wall stiffness of 34 BD patients and 28 age/sex-matched controls. CIMT measurements were performed from the posterior wall of the carotid artery approximately 10 mm proximal to the initiation of the carotid bulb using B-mode ultrasound. The stiffness of the CCA was measured from the superficial wall of the CCA using shear wave elastography (SWE). SWE measurements were recorded as shear wave velocity (SWV) using m/s as a unit.Results: The mean right (0.5±0.11 mm) and left (0.5±0.14 mm) CIMT of the patients were significantly higher compared to the mean right (0.41±0.07 mm) and left (0.41±0.11 mm) CIMT of the healthy controls (p=0.001 and p= 0.003 respectively). The mean right (3.72±0.94 m/s) and left (3.57±0.72 m/s) CCA wall stiffness of the patients were significantly higher compared to the mean right (2.42±0.49 m/s) and left (2.56±0.49 m/s) CCA wall stiffness of the controls (p<0.001 for both).Conclusions: SWE seems to be a promising modality to evaluate endothelial dysfunction in BD by interpreting the arterial stiffness, and SWE might be an important adjunct to clinical and laboratory findings, and imaging modalities to assess cardiovascular risk in BD. Moreover, SWE evaluation of the arterial stiffness might assist us to understand pathophysiological aspects of BD.

Pseudoaneurysm of a cystic artery is a rare entity that commonly occurs secondary to biliary procedures. Most of the cases in literature are consisted of ruptured aneurysms and to our knowledge, except our case, there were only 3 cases with unruptured aneurysms, which incidentally were detected by radiological methods. When cystic artery pseudoaneurysm is present with acute cholecystitis, most of the reports in literature suggested open cholecystectomy with the ligation of the cystic artery as a main treatment option. In this paper we present a case of acute cholecystitis with unruptured cystic artery pseudoaneurysm that incidentally was detected by computed tomography (CT). Cystic artery pseudoaneurysm was handled laparoscopically with simultaneous cholecystectomy. Due to high risk of rupture, surgeons have evaded laparoscopic approach to acute cholecystitis, which accompanied cystic artery pseudoaneurysm. However herein, we proved that laparoscopic management of cystic artery pseudoaneurysm with simultaneous cholecystectomy is feasible and reliable method.

To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test its performance in a prospective independent sample consisting of consecutive real-world patients. All consecutive patients who underwent emergency non-contrast-enhanced head CT in five different centers were retrospectively gathered. Five neuroradiologists created the ground-truth labels. The development dataset was divided into the training and validation set. After the development phase, we integrated the deep learning model into an independent center’s PACS environment for over six months for assessing the performance in a real clinical setting. Three radiologists created the ground-truth labels of the testing set with a majority voting. A total of 55,179 head CT scans of 48,070 patients, 28,253 men (58.77%), with a mean age of 53.84 ± 17.64 years (range 18–89) were enrolled in the study. The validation sample comprised 5211 head CT scans, with 991 being annotated as ICH-positive. The model's binary accuracy, sensitivity, and specificity on the validation set were 99.41%, 99.70%, and 98.91, respectively. During the prospective implementation, the model yielded an accuracy of 96.02% on 452 head CT scans with an average prediction time of 45 ± 8 s. The joint CNN-RNN model with an attention mechanism yielded excellent diagnostic accuracy in assessing ICH and its subtypes on a large-scale sample. The model was seamlessly integrated into the radiology workflow. Though slightly decreased performance, it provided decisions on the sample of consecutive real-world patients within a minute.