This study aims to develop a deep learning–based image processing technology that converts medical spinal images using artificial intelligence (AI). The model is designed to learn features from various spinal imaging datasets and enable transformation between different imaging modalities. Through this approach, the system can generate medical images that more clearly represent spinal anatomical structures and lesion information. Training strategies are also applied to preserve structural consistency and diagnostic details across images. This technology can improve the accuracy of image analysis for spinal disease diagnosis and support clinicians in the diagnostic process. Furthermore, it is expected to reduce the need for additional scans and enhance the efficiency of medical image utilization.
Project Period
2024~2025
This study aims to develop a deep learning–based image processing technology that converts medical spinal images using artificial intelligence (AI). The model is designed to learn features from various spinal imaging datasets and enable transformation between different imaging modalities. Through this approach, the system can generate medical images that more clearly represent spinal anatomical structures and lesion information. Training strategies are also applied to preserve structural consistency and diagnostic details across images. This technology can improve the accuracy of image analysis for spinal disease diagnosis and support clinicians in the diagnostic process. Furthermore, it is expected to reduce the need for additional scans and enhance the efficiency of medical image utilization.