A Novel Dual-Output Deep Learning Model Based on InceptionV3 for Radiographic Bone Age and Gender Assessment.

Abstract

Hand-wrist radiographs are used in bone age prediction. Computer-assisted clinical decision support systems offer solutions to the limitations of the radiographic bone age assessment methods. In this study, a multi-output prediction model was designed to predict bone age and gender using digital hand-wrist radiographs. The InceptionV3 architecture was used as the backbone, and the model was trained and tested using the open-access dataset of 2017 RSNA Pediatric Bone Age Challenge. A total of 14,048 samples were divided to training, validation, and testing subsets with the ratio of 7:2:1, and additional specialized convolutional neural network layers were implemented for robust feature management, such as Squeeze-and-Excitation block. The proposed model achieved a mean squared error of approximately 25 and a mean absolute error of 3.1 for predicting bone age. In gender classification, an accuracy of 95% and an area under the curve of 97% were achieved. The intra-class correlation coefficient for the continuous bone age predictions was found to be 0.997, while the Cohen's $\kappa$ coefficient for the gender predictions was found to be 0.898 ( $p<$ 0.001). The proposed model aims to increase model efficiency by identifying common and discrete features. Based on the results, the proposed algorithm is promising; however, the mid-high-end hardware requirement may be a limitation for its use on local machines in the clinic. The future studies may consider increasing the dataset and simplification of the algorithms.