Report of Clinical Bone Age Assessment using Deep Learning for an Asian population in Taiwan

• Published in: Biomedicine (Taipei) Vol. 11; no. 3; pp. 50 - 58
• Main Authors: Cheng, Chi Fung, Huang, Eddie Tzung-Chi, Kuo, Jung-Tsung, Liao, Ken Ying-Kai, Tsai, Fuu‑Jen
• Format: Journal Article
• Language: English
• Published: China (Republic : 1949- ) China Medical University 01.01.2021
• Subjects: Original; Deep learning; Bone age assessment; Artificial intelligence
• ISSN: 2211-8039; 2211-8020; 2211-8039
• DOI: 10.37796/2211-8039.1256

A deep learning-based automatic bone age identification system (ABAIs) was introduced in medical imaging. This ABAIs enhanced accurate, consistent, and timely clinical diagnostics and enlightened research fields of deep learning and artificial intelligence (AI) in medical imaging. The goal of this study was to use the Deep Neural Network (DNN) model to assess bone age in months based on a database of pediatric left-hand radiographs. The Inception Resnet V2 model with a Global Average Pooling layer to connect to a single fully connected layer with one neuron using the Rectified Linear Unit (ReLU) activation function consisted of the DNN model for bone age assessment (BAA) in this study. The medical data in each case contained posterior view of X-ray image of left hand, information of age, gender and weight, and clinical skeletal bone assessment. A database consisting of 8,061 hand radiographs with their gender and age (0-18 years) as the reference standard was used. The DNN model's accuracies on the testing set were 77.4%, 95.3%, 99.1% and 99.7% within 0.5, 1, 1.5 and 2 years of the ground truth respectively. The MAE for the study subjects was 0.33 and 0.25 year for male and female models, respectively. In this study, Inception Resnet V2 model was used for automatic interpretation of bone age. The convolutional neural network based on feature extraction has good performance in the bone age regression model, and further improves the accuracy and efficiency of image-based bone age evaluation. This system helps to greatly reduce the burden on clinical personnel.