Computed tomography angiography (CTA) is an efficient method for the diagnosis of heart disease. However, few contemporary studies have evaluated the prognostic value of three-dimensional (3D)-CTA for patients with acute coronary artery disease. The aim of the present study was to investigate the diagnostic value of 3D-CTA for patients with acute coronary artery disease. A total of 136 patients with suspected acute coronary artery disease were recruited and received conventional coronary angiography (CCA) and 3D-CTA. 3D-CTA was used to assess calcified plaques in the coronary arteries (CCTA), the ratio of calcified plaque volume to vessel circumference (RVTC) and diagnostic accuracy. The results revealed that 3D-CTA was a more effective diagnostic method for identifying calcified plaques in patients with acute coronary artery disease compared with CCA. 3D-CTA demonstrated a significantly better area under curve, sensitivity, specificity, positive predictive value and negative predictive value compared with CCA (P<0.01). In the present study, 3D-CTA was used to successfully diagnose 86 patients with acute coronary artery disease, 34 with myocardial infarction and 16 with stable angina. 3D-CTA images clearly showed global noise levels and target-to-background ratios determined by manually delineated coronary plaque lesions compared with CCA. Furthermore, 3D-CTA was significantly better for discriminating ischemia compared with CCA (P<0.01). In conclusion, the results of the present study suggest that 3D-CTA provides superior diagnostic performance compared with CCA alone in patients with acute coronary artery disease.

This study was aimed to explore the adoption value of magnetic resonance imaging (MRI) under convolutional neural networks (CNN) in the therapeutic effect of nasopharyngeal carcinoma (NPC) radiotherapy. A total of 54 NPC patients were recruited. CNN was employed to perform 3D visualization processing on magnetic resonance (MR) images of NPC patients. MRI changes were analyzed before and after the patient received radiotherapy. The image segmentation and radiotherapy effects of CNN were evaluated by the Recall, intersection over union (IOU), postoperative apparent diffusion coefficient (ADC), and diagnostic coincidence rate. Moreover, gradient vector flow (GVF) algorithm, fuzzy c-means (FCM), and SegNet were adopted for comparative evaluation. Recall of CNN was 94.89% and the IOU was 84.16%, which was remarkably different from other algorithms (P < 0.05). After analysis of the MRI images of patients receiving radiotherapy, ADC of local residual patients was 1.108 ± 0.097 measured by CNN, the ADC was 1.826 ± 0.115, and the missed diagnosis rate was only 7.14%. In summary, CNN had a good effect on the localization and segmentation of NPC patients, and can accurately evaluate the effect of patients receiving radiotherapy, which can assist clinical diagnosis and treatment of NPC.