  This study applies a technique to expand the number of images to a level that
allows deep learning. And the applicability of the Sauvegrain method through
deep learning with relatively few elbow X-rays is studied. The study was
composed of processes similar to the physicians' bone age assessment
procedures. The selected reference images were learned without being included
in the evaluation data, and at the same time, the data was extended to
accommodate the number of cases. In addition, we adjusted the X-ray images to
better images using U-Net and selected the ROI with RPN + so as to be able to
perform bone age estimation through CNN. The mean absolute error of the
Sauvegrain method based on deep learning is 2.8 months and the Mean Absolute
Percentage Error (MAPE) is 0.018. This result shows that X - ray analysis using
the Sauvegrain method shows higher accuracy than that of the age group of
puberty even in the deep learning base. This means that deep learning of the
Suvegrain method can be measured at a level similar to that of an expert, based
on the extended X-ray image with the image data extension technique. Finally,
we applied the Sauvegrain method to deep learning for accurate measurement of
bone age at puberty. As a result, the present study is based on deep learning,
and compared with the evaluation results of experts, it is possible to overcome
limitations of the method of measuring bone age based on machine learning which
was in TW3 or Greulich & Pyle due to lack of X- I confirmed the fact. And we
also presented the Sauvegrain method, which is applicable to adolescents as
well.
