Abstract：Objective To study and compare the results to prove that it takes a long time to solve the problems of manual bone age
interpretation, such as high human subjective influence, poor consistency and stability of results, etc. Methods G-P map, TW score
method, Zhonghua 05 and other methods were used to compare bone age X-ray images. Artificial intelligence, artificial interpretation
and artificial intelligence-assisted artificial interpretation were studied, and the differences among multiple readers were studied.
Results Based on the standard TW3, 250 bone age images of children were compared by artificial intelligence system and doctor,
TW3-AI model interpretation efficiency on the average processing time was 1.5±0.2 s, significantly shorter than the doctor’s
525.6±55.5 s. In terms of accuracy and reliability, the root mean square of TW3-AI model and expert interpretation results
was 0.50 years, indicating a high degree of consistency between the two. Based on G-P standard, the bone age of 745 patients with
abnormal growth and development was estimated. The average time of doctors’ interpretation was about 2 min, and the AI model only
needed 1~2 s. In terms of accuracy, the average proportion of the AI system was less than one year from the gold standard, 84.60%.
Based on the Chinese 05 standard, the average time of manual group reading was significantly higher than that of AI conformance
assisted assessment. Conclusion The intelligent detection system of children’s bone age can complete the imaging analysis of
children’s bone age at the second level and provide quantitative results such as ossification center rating and bone age, so as to assist
doctors in rapid disease diagnosis and efficacy evaluation, and provide decision-making basis for the diagnosis and treatment of
children’s endocrine diseases.
孙梦莎1，丁永红1，颜子夜1,2，苏晓鸣1. 人工智能在儿童骨龄影像检测中的应用[J]. 中国医疗设备, 2021, 36(3): 28-32.
SUN Mengsha1，DING Yonghong1，YAN Ziye1,2，SU Xiaoming1. Application of Artificial Intelligence in Evaluating the Bone Age Image of Children. China Medical Devices, 2021, 36(3): 28-32.
