A Novel Dual-Output Deep Learning Model Based on InceptionV3 for Radiographic Bone Age and Gender Assessment.
Authors
Affiliations (5)
Affiliations (5)
- Biomedical Engineering Department, Institute of Graduate Studies, Istanbul University-Cerrahpasa, 34320, Istanbul, Turkey. [email protected].
- Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, İstanbul University-Cerrahpaşa, 34098, Istanbul, Turkey.
- CAST (Cerrahpaşa Research, Simulation and Design Laboratory), Istanbul University-Cerrahpaşa, 34098, İstanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, 34220, Istanbul, Turkey.
- Biomedical Device Technology Program, Vocational School of Technical Sciences, Istanbul University-Cerrahpasa, 34500, Istanbul, Turkey.
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 <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>κ</mi></math> coefficient for the gender predictions was found to be 0.898 ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>p</mi> <mo><</mo></mrow> </math> 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.