BACKGROUND AND PURPOSE: Comparison of the diagnostic performance for thyroid cancer on ultrasound between a convolutional neural network and visual assessment by radiologists has been inconsistent. Thus, we aimed to evaluate the diagnostic performance of the convolutional neural network compared with the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) for the diagnosis of thyroid cancer using ultrasound images. MATERIALS AND METHODS:From March 2019 to September 2019, seven hundred sixty thyroid nodules ($10 mm) in 757 patients were diagnosed as benign or malignant through fine-needle aspiration, core needle biopsy, or an operation. Experienced radiologists assessed the sonographic descriptors of the nodules, and 1 of 5 American College of Radiology TI-RADS categories was assigned. The convolutional neural network provided malignancy risk percentages for nodules based on sonographic images. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated with cutoff values using the Youden index and compared between the convolutional neural network and the American College of Radiology TI-RADS. Areas under the receiver operating characteristic curve were also compared. RESULTS:Of 760 nodules, 176 (23.2%) were malignant. At an optimal threshold derived from the Youden index, sensitivity and negative predictive values were higher with the convolutional neural network than with the American College of Radiology TI-RADS (81.8% versus 73.9%, P ¼ .009; 94.0% versus 92.2%, P ¼ .046). Specificity, accuracy, and positive predictive values were lower with the convolutional neural network than with the American College of Radiology TI-RADS (86.1% versus 93.7%, P , .001; 85.1% versus 89.1%, P ¼ .003; and 64.0% versus 77.8%, P , .001). The area under the curve of the convolutional neural network was higher than that of the American College of Radiology TI-RADS (0.917 versus 0.891, P ¼ .017). CONCLUSIONS:The convolutional neural network provided diagnostic performance comparable with that of the American College of Radiology TI-RADS categories assigned by experienced radiologists. ABBREVIATIONS: ACR ¼ American College of Radiology; AUC ¼ area under the curve; AI ¼ artificial intelligence; CNB ¼ core needle biopsy; CNN ¼ convolutional neural network; FNA ¼ fine-needle aspiration; ROC ¼ receiver operating characteristic; TI-RADS ¼ Thyroid Imaging and Reporting and Data System; TR ¼ category of TI-RADS; US ¼ ultrasound

This article describes the design and performance testing of a turbine bypass valve trim. For effective control of velocity and pressure, a trim designed to have a tortuous path was designed. Computational fluid dynamics and FEM analyses were used in the design process. The valve, which was installed the designed trim, was tested. To evaluate its performance in the field, the valve was installed at a 900 MW combined power plant system for three months. The results showed that the pressure letdown was successfully controlled by the designed trim, and the noise level was reduced by about 8.1 per cent compared with the previous trim-type valve.