Automatic bone age assessment: a deep learning case study on the Brazilian population with a supporting mobile application prototype

Bone Age Assessment is essential for monitoring physiologic and abnormal growth of children and adolescents, allowing the identification of skeletal maturation. Traditional methods, such as Greulich and Pyle, are widely used but have limitations due to the variability among evaluators, a consequence of the inherent subjectivity of visual analysis. This paper presents a comprehensive end-to-end AI-based mobile prototype as a solution that combines methodological innovation with practical application. To achieve this, we developed a new deep learning architecture that integrates VGG16, Feature Pyramid Network, a convolutional block attention module, and metadata to enhance model performance. The model was trained using a novel dataset curated exclusively from healthy individuals within the Brazilian population, ensuring data reliability through a rigorous selection process. Subsequently, the model is embedded in a fullyfunctional mobile application, enabling real-time and standardized bone age estimation. The system automatically implements the Greulich and Pyle method, demonstrating the potential of the proposed AI-based mobile app prototype to substantially reduce diagnostic variability. Experimental results show a mean absolute difference of 8.7 months overall and 7.2 months for individuals aged 6 to 18 years, highlighting the potential of our approach to enhance accuracy, efficiency, and reproducibility in clinical practice.