Construction and clinical preliminary validation of an automaticbone age assessment model based on deep learning

Abstract

Objective To build an automatic bone age assessment system based on China 05 Bone Age Standard and the latest deep learning technology, and preliminary clinical verification was carried out. Methods The left-hand radiographs of 5 000 children with suspected metabolic disorders were acquired from Wuxi Children′s Hospital. Among these cases, 2 351 patients were randomly chosen as training set, and 101 patients were randomly used as validation set. Four professional pediatric radiologists annotated the development stage according to the China 05 RUS-CHN standard with double-blind method. The mean value of the bone age assessed by experts was the reference standard which was used to train and validate the deep learning mothods based artificial intelligence (AI) model. Accuracy, mean absolute error (MAE), root mean squared error (RMSE) and time efficiency of bone age assessment were compared by using Chi-square test and t test and F test between resident doctors and AI model in the validation set. Results The MAE and RMSE was (0.37±0.35) years and 0.50 years between AI model and reference standard, respeactively. When the error range was within ±1.0, ±0.7 and ±0.5 years, the accuracy of model on the validation set was 94.1% (95/101), 89.1% (90/101), 74.3% (75/101) respectively. The accuracy between two resident doctors and AI prediction wasn′t statistical significant (P>0.05). Conclusion The AI model of bone age assessment based on deep learning is feasible and has the characteristics of high accuracy and efficiency. Key words: Bone age; Deep learning; Artificial intelligence; China 05 method