Carotid artery stenting for stenosis after carotid artery replacement: An illustrative case report.

Background: There are few reports on the treatment of carotid artery stenosis after arterial vessel replacement. We report and discuss an illustrative case of carotid artery stenting (CAS) performed for stenosis after carotid artery replacement. Case Description: A woman in her 20s experienced injury to the right carotid artery during an operation for removal of a carotid body tumor 6 years before presentation. The right common carotid artery and internal carotid artery were replaced with an artificial vessel graft at that time. Intraluminal stenosis in the graft was not identified 3 years after surgery; however, 4 years after surgery, stenosis was recognized at the non-anastomotic site inside the artificial vessel graft. Subsequently, antiplatelet therapy was initiated. The stenosis was noted to progress gradually in follow-up appointments. Therefore, we decided to intervene because of the patient's young age and the risk of long-term hemodynamic stress. Angiography revealed pseudo-occlusion in the artificial vessel. Percutaneous transluminal angioplasty was performed for stenosis with distal protection; subsequently, CAS was performed. The patient was discharged without neurological deficits 4 days after the operation, and no apparent restenosis was observed as of the 1-year follow-up. Conclusion: Stenosis after cervical artery replacement can be safely treated with CAS. Inflation pressure and stent should be selected according to the pathology of the stenosis.

Two patients of visual disturbance and optic perineuritis after placement of a flow diverter.

The pathogenesis of new visual symptoms after flow diverter stent placement in the ophthalmic artery for internal carotid artery aneurysms remains unclear. We report two cases of patients who developed visual field disturbance and decreased visual acuity following flow diverter placement. The "doughnut sign" was found around the optic nerve on magnetic resonance imaging. The patients had progressive visual field defects and impairment on the side where the flow diverter was placed. Short tau inversion recovery coronal images showed a doughnut-shaped high-signal around the optic nerve on the affected side. Both patients were treated with steroid pulse therapy, and 1 received endovascular therapy. Their symptoms gradually improved, and the "doughnut sign" disappeared. The "doughnut sign" observed around the optic nerve on magnetic resonance imaging may be found alongside visual disturbance symptoms after paraclinoid aneurysm treatment. It is recommended that short tau inversion recovery sequences be performed preoperatively in patients presenting with visual impairment and in whom the possibility of postoperative exacerbation is suspected.

Classification of teeth in cone-beam CT using deep convolutional neural network.

Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification.