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.
