High accuracy and precision bone age assessment is very important for the diagnosis and treatment monitoring of various pediatric diseases. The commonly used bone age assessment methods include GP atlas, TW3 score and Zhonghua 05. GP method is to compare wrist X-ray films with atlas reference X-ray films. Its main disadvantages are strong subjectivity and long atlas standard interval. Different from GP method, TW3 method is to grade and score each bone, add each epiphyseal score to calculate the total score of bone maturity, and obtain the corresponding final bone age value. Although TW3 scoring method is relatively accurate, it is complex and time-consuming, and there is great variability among evaluators. In order to evaluate bone age more efficiently and accurately, a method based on computer image automatic recognition technology can help to overcome these problems.

In this study, 1000 children aged 1-18 in 5 hospitals are selected as the research objects. 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.

The purpose of this study is to use the most advanced artificial intelligence deep learning bone age evaluation software to explore the value of bone age instrument to improve the accuracy and diagnostic efficiency of bone age evaluation by pediatricians.

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.

Inclusion Criteria:

1. Children aged 1 to 18 years.
2. Clinical diagnosis of requiring bone age examination.

Exclusion Criteria:

1. There are obstructions or implants in the wrist bone, such as metal.
2. Poor resolution, which affects the observation of bone characteristics.
3. Poor shooting position of the wrist due to various reasons.

Children aged 1-18 who need bone age assessment