A pictorial view of the three-dimensional representation and comparative two-dimensional image orientation derived from computed tomography angiography in a dog with a patent ductus arteriosus

Key considerations in the approach to congenital heart disease in dogs and cats

Congenital heart disease is an important subset of all cardiovascular disease in dogs and cats that is present at birth and most often detected in young animals but can be diagnosed in adulthood. The range of abnormalities that can occur during development of the heart is vast incorporating simple and complex defects, varying degrees of severity and clinical presentations that include heart failure and cyanosis. While some defects do not result in morbidity in an individual animal, others cause severe clinical signs and death at a young age. Advances in imaging and expanding treatment options offer increasingly more possibilities in the diagnosis and management of congenital heart disease which is the focus of this review. The objective is to provide a broad overview of current practice and highlight key aspects to guide practitioners in their approach to congenital heart disease diagnosis and knowledge of available treatment options.

The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology

As the development of artificial intelligence (AI) technology, the deep-learning (DL)-based Virtual Reality (VR) technology, and DL technology are applied in human-computer interaction (HCI), and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body motions based on the convolutional neural network (CNN) algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7-9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically.