RECONSTRUCTION AND ANALYSIS OF HUMAN ANATOMY MODELS USING NEURAL NETWORKS

Abstract

The application of neural networks in reconstructing and analyzing human anatomical models represents a major advancement in medical imaging and computational anatomy. By leveraging deep learning algorithms such as convolutional and generative adversarial networks, it becomes possible to recreate highly accurate three-dimensional representations of the human body from MRI, CT, and ultrasound data. These intelligent systems enable automated segmentation, structure recognition, and real-time visualization of organs and tissues. As a result, neural networks not only reduce the time required for anatomical modeling but also improve diagnostic precision and educational visualization. This study explores the role of neural networks in digital anatomy, focusing on their effectiveness in reconstructing and interpreting human anatomical structures for both clinical and educational purposes.