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ObjectiveChronic subdural hematoma (CSDH) is one of the most common types of intra-cranial hemorrhages usually associated with trauma. Surgical treatment is the treatment of choice and burr hole trephination (BHT) is widely performed. The recurrence rate in the patients with CSDH is 3.7-30%. This study investigated the risk factors associated with the recurrence of patients with CSDH who underwent BHT.MethodsOne hundred twenty-five patients with CSDH underwent BHT. Eight of 125 patients (6.4%) underwent reoperation for recurrent CSDH. We retrospectively analyzed demographic, clinical and radiological findings, catheter tip location and drainage duration as the risk factors for the recurrence of CSDH.ResultsRecurrence of CSDH in the high- or mixed-density groups was significantly higher than those in the low- or iso-density groups (p<0.001). Placement of catheter tip at the temporoparietal area was associated with a significantly higher recurrence rate of CSDH than placement at the frontal area (p=0.006) and the brain re-expansion rate (BRR) was much lower than placement at the frontal area (p<0.001).ConclusionThe operation may be delayed in high- and mixed-density groups, unless severe symptoms or signs are present. In addition, placing the catheter tip at the frontal area helps to reduce the incidence of postoperative recurrence of CSDH and to increase the BRR.

Objective: The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Methods: Panoramic and cone beam CT (CBCT) images obtained from June 2018 to May 2020 were screened and 1020 patients were selected. Our dataset of 2040 sound mandibular second molars comprised 887 C-shaped canals and 1153 non-C-shaped canals. To confirm the presence of a C-shaped canal, CBCT images were analyzed by a radiologist and set as the gold standard. A CNN-based deep-learning model for predicting C-shaped canals was built using Xception. The training and test sets were set to 80 to 20%, respectively. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, and precision. Receiver-operating characteristics (ROC) curves were drawn, and the area under the curve (AUC) values were calculated. Further, gradient-weighted class activation maps (Grad-CAM) were generated to localize the anatomy that contributed to the predictions. Results: The accuracy, sensitivity, specificity, and precision of the CNN model were 95.1, 92.7, 97.0, and 95.9%, respectively. Grad-CAM analysis showed that the CNN model mainly identified root canal shapes converging into the apex to predict the C-shaped canals, while the root furcation was predominantly used for predicting the non-C-shaped canals. Conclusions: The deep-learning system had significant accuracy in predicting C-shaped canals of mandibular second molars on panoramic radiographs.

Although the capability of the computer has been developed and numerical algorithms have been advanced, automobile crash optimization is still quite difficult owing to high non-linearity and numerical cost. Therefore, metamodel-based optimization methods have been frequently utilized in crashworthiness optimization. However, the methods have various limits on the number of design variables and precision. The equivalent-static-loads (ESLs) method has been proposed to overcome the limitations. ESLs are static loads which generate the same displacement field in static analysis as the displacement field at each time step in non-linear dynamic analysis; they are used as the external loads for linear static response optimization. The results of linear static response optimization are utilized to update the design, and non-linear dynamic analysis is performed again with the updated design. The process proceeds in an iterative manner until the convergence criteria are satisfied. From various research studies on the ESLs method, it has been demonstrated that the ESLs method is fairly useful. An automobile frontal structure is optimized for the pendulum test. The optimization problem is formulated with many design variables including displacement, velocity, and acceleration constraints. A method is proposed for handling the velocity and acceleration constraints by using the finite difference method. In a numerical analysis of the pendulum test, the velocity and acceleration are extremely non-linear and noisy. Thus, a filtering technique is utilized for the displacement, velocity, and acceleration curves. LS-DYNA is used for non-linear dynamic analysis, and NASTRAN is used for linear static response optimization and generation of ESLs. The SAE 60 filter in LS-PRE/POST is used to filter the displacement response. A program is developed for interfacing the two systems.