An X-ray bone age assessment method for hands and wrists of adolescents in Western China based on feature fusion deep learning models.

The epiphyses of the hand and wrist serve as crucial indicators for assessing skeletal maturity in adolescents. This study aimed to develop a deep learning (DL) model for bone age (BA) assessment using hand and wrist X-ray images, addressing the challenge of classifying BA in adolescents. The results of this DL-based classification were then compared and analyzed with those obtained from manual assessment. A retrospective analysis was conducted on 688 hand and wrist X-ray images of adolescents aged 11.00-23.99 years from western China, which were randomly divided into training set, validation set and test set. The BA assessment results were initially analyzed and compared using four DL network models: InceptionV3, InceptionV3 + SE + Sex, InceptionV3 + Bilinear and InceptionV3 + Bilinear. + SE + Sex, to identify the DL model with the best classification performance. Subsequently, the results of the top-performing model were compared with those of manual classification. The study findings revealed that the InceptionV3 + Bilinear + SE + Sex model exhibited the best performance, achieving classification accuracies of 96.15% and 90.48% for the training and test set, respectively. Furthermore, based on the InceptionV3 + Bilinear + SE + Sex model, classification accuracies were calculated for four age groups (< 14.0 years, 14.0 years ≤ age < 16.0 years, 16.0 years ≤ age < 18.0 years, ≥ 18.0 years), with notable accuracies of 100% for the age groups 16.0 years ≤ age < 18.0 years and ≥ 18.0 years. The BA classification, utilizing the feature fusion DL network model, holds significant reference value for determining the age of criminal responsibility of adolescents, particularly at the critical legal age boundaries of 14.0, 16.0, and 18.0 years.