An accurate pediatric bone age prediction model using deep learning and contrast conversion

Abstract

Objectives: This study aimed to develop an accurate pediatric bone age prediction model by utilizing deep learning models and contrast conversion techniques, in order to improve growth assessment and clinical decision -making in clinical practice. Methods: The study employed a variety of deep learning models and contrast conversion techniques to predict bone age. The training dataset consisted of pediatric left-hand X-ray images, each annotated with bone age and sex information. Deep learning models, including a convolutional neural network (CNN), ResNet 50, VGG 19, Inception V3, and Xception were trained and assessed using the mean absolute error (MAE). For the test data, contrast conversion techniques including FCE, CLAHE, and HE were implemented. The quality of the images was evaluated using PSNR, MSE, SNR, COV, and CNR metrics. The bone age prediction results using the test data were evaluated based on the MAE and root mean square error (RMSE), and the t -test was performed. Results: The Xception model showed the best performance (MAE=41.12). HE exhibited superior image quality, with higher SNR and COV values than other methods. Additionally, HE demonstrated the highest contrast among the techniques assessed, with a CNR value of 1.29. Improvements in bone age prediction resulted in a decline in MAE from 2.11 to 0.24, along with a decrease in RMSE from 0.21 to 0.02. Conclusion: This study demonstrates that preprocessing the data before model training does not significantly affect the performance of bone age prediction when comparing contrastconverted images with original images.