Bone age assessment plays an important role in monitoring the growth and development of children. However, the application of traditional X-ray imaging techniques is often affected by background interference, which reduces the accuracy of the assessment results. In recent years, deep learning technology has been widely applied in the field of bone age image processing, demonstrating significant advantages. This paper reviews the progress of deep learning in addressing background redundancy in bone age imaging. It focuses on methods such as region of interest extraction, background segmentation techniques, and attention mechanisms. Research indicates that these methods can effectively eliminate redundant background information, enhancing the accuracy and efficiency of bone age assessments. Additionally, this paper discusses the limitations of existing technologies and future development directions, aiming to provide new research insights and practical guidance for the field of bone age assessment.