Deep Learning Approach Applied to Prediction of Bone Age Based on Computed Tomography Orthopedic Image Processing

Objective: Deep learning and neural network models are new research directions in the field of machine learning and artificial intelligence. Deep learning has made breakthroughs in image recognition and speech recognition applications, and has also shown unique advantages in face recognition and information retrieval, and has been widely used. Methods: Thin-layer computed tomography (CT) scan and multiplanar reconstruction (MPR) and volume reconstruction (VR) techniques were used to perform CT thin-slice scan and volume of the bilateral clavicle sternum at 548 number of l5~25 years old. Reproduction (volume rendering, VR) and three-dimensional image recombination, measuring and calculating the longest diameter of the sternal end of the bilateral clavicle, the longest diameter of the metaphysis and its length ratio, the area of the epiphysis, the area of the metaphysis and its area ratio, etc. We establish a mathematical model of bone age inference, and then substitute 50 training samples into the mathematical model to test the accuracy of the model. Results: There was a statistically significant difference between the male and female sex ratios in the same age group (P < 0.05). The established mathematical model shows that the developmental law of the sternal skeletal bone is highly correlated with the biological age. The accuracy of all models is 70.5% (±1.0 years old) and 82.5% (±1.5 years). Skeletal X-ray images show different gradation changes in black and white, with black-and-white contrast and hierarchical image features. Based on the advantages of deep learning in image recognition, we combine it with bone age assessment research to build a forensic bone age automation. Conclusions: This paper harnesses the basic concepts of deep learning and its network structure, and expounds the research progress of deep learning in image recognition in different research fields at home and abroad in recent years, as well as the advantages and application prospects of deep learning in bone age assessment.