Performance of the Diagnostic Value of Bone Age Assessment Software Based on Deep Learning in Chinese Children

1. Study subjects: Radiographs of the left wrist of 1000 children aged 1-18 years who underwent bone age measurement at 5 hospitals were selected 2. Research methods: 2.1 general information: name, gender, actual age, height, weight, BMI, race, disease type 2.2 main instrument: artificial intelligence X-ray bone age instrument 2.3 scanning scheme: Left posterior and anterior radiographs 2.4 bone age evaluation method and group 2.4.1 the standard group：Tw3-RUS and TW3-CARPAL were used to evaluate the left hand bone age tablets by the 5 pediatric doctors designated in the TW3 system training. There was no time limit for reading the tablets. The mean value of the evaluation results of the 5 doctors was taken as the final reference standard. 2.4.2 Grouping of bone age assessment ① artificial group (5 assessors assess bone age separately) ② AI Group③ artificial + AI Group (artificial Group assesses bone age with the assistance of AI intelligent software assessment) After taking bone age films with bone age instrument, the film reading results and evaluation time of AI Group, artificial group and standard group are recorded. One month later, the artificial group re-analyzes 1000 films with the assistance of AI system, and the evaluation time is recorded. Finally, the accuracy and time difference of artificial group, AI Group, artificial combined AI Group and standard group are compared.