Transcatheter left ventricular assist pump (LVAP) is widely used in cardiogenic shock, post-circulatory hypoperfusion syndrome and high-risk percutaneous coronary intervention (PCI), and its application scenarios cover various complex environments such as ICU, operating room, emergency department and catheterization room. It is important to quickly and accurately implant the transcatheter LVAP and monitor its position in real time. This paper reported 2 male patients with high-risk PCI, aged 47 and 45 years old, both with triple coronary artery disease and reduced ejection fraction (<35%). The domestically produced transcatheter LVAP was implanted using an echocardiography guidance technology system, and PCI treatment was performed under the assistance of the interventional pump. The operation was smooth, and the interventional pump assisted for 1 h in 2 patients, and the pump was successfully removed. There were no obvious complications related to the pump and PCI after the operation, and the patients were discharged smoothly.
This article aims to combine deep learning with image analysis technology and propose an effective classification method for distal radius fracture types. Firstly, an extended U-Net three-layer cascaded segmentation network was used to accurately segment the most important joint surface and non joint surface areas for identifying fractures. Then, the images of the joint surface area and non joint surface area separately were classified and trained to distinguish fractures. Finally, based on the classification results of the two images, the normal or ABC fracture classification results could be comprehensively determined. The accuracy rates of normal, A-type, B-type, and C-type fracture on the test set were 0.99, 0.92, 0.91, and 0.82, respectively. For orthopedic medical experts, the average recognition accuracy rates were 0.98, 0.90, 0.87, and 0.81, respectively. The proposed automatic recognition method is generally better than experts, and can be used for preliminary auxiliary diagnosis of distal radius fractures in scenarios without expert participation.
Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, QAB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.