A Study of the CHN Intelligent Bone Age Assessment Method with Reference to Atlas Developmental Indications

Bone Age (BA) is one of the most important indicators in evaluating children's growth. The Bone Age Assessment (BAA) based on Chinese wrist bone development standard-CHN (CHN) scoring method is widely used in the evaluation of children's growth and development and height prediction. However, the adjacent developmental levels of some reference bones last longer, leading to the subjective judgment of developmental levels by experts based on personal experience, which affects the accuracy of predictions. When deep learning is used to evaluate the developmental levels of these atlases, the prediction results will be random. In this paper, based on more than 20 000 X-ray images evaluated by experts, a new mature indicator with a large interval with a large interval is drawn to generate exquisite atlas to perform some reference bones. Additionally, the corresponding maturity score is determined by analyzing the level structure process to maximize the impact of error-level prediction on BAA. Combining Harris features and convolutional blocks of the convolutional neural network of the attention module is designed to evaluate automatically the level of bone maturity. In addition, an annotated database with an age distribution of 5-11 years is built to train and evaluate the method. The accuracy of predictions obtained by adding a new standard atlas to the CHN method reaches 94.6% and 99.13% when the tolerance is 0.5 years and 1 year, respectively. The experimental results show that the method proposed in this paper can distinguish the development degree of reference bones more precisely, and improve greatly the accuracy of BAA, proving the potential for practical clinical application.