Bone Age Estimation and Prediction of Final Adult Height Using Deep Learning

Abstract

Purpose: The appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to predict the final adult height of Korean children.

Materials and Methods: A total of 1678 radiographs from 866 children, for which the interpretation results were consistent be tween two pediatric endocrinologists, were used to train and validate the deep learning model. The bone age estimation algorithm was based on the convolutional neural network of the deep learning system. The test set simulation was performed by a deep learning program and two raters using 150 radiographs and final height data for 100 adults.

Results: There was a statistically significant correlation between bone age interpreted by the artificial intelligence (AI) program and the reference bone age in the test set simulation (r=0.99, p<0.001). In the test set simulation, the AI program showed a mean absolute error (MAE) of 0.59 years and a root mean squared error (RMSE) of 0.55 years, compared with reference bone age, and showed similar accuracy to that of an experienced pediatric endocrinologist (rater 1). Prediction of final adult height by the AI program showed an MAE of 4.62 cm, compared with the actual final adult height.

Conclusion: We developed a bone age estimation program based on a deep learning algorithm. The AI-derived program demon strated high accuracy in estimating bone age and predicting the final adult height of Korean children and adolescents.