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We first present the mobile app prototype developed to integrate the AI models and support clinicians in bone age assessment.
We first present the mobile app prototype developed to integrate the AI models and support clinicians in bone age assessment.
We first present the mobile app prototype developed to integrate the AI models and support clinicians in bone age assessment. Next, we outline the dataset and labeling procedures, followed by the data.
We developed digiBONE, a deep learning framework that models segment-specific skeletal maturation. Hand radiographs were segmented into anatomically coherent regionsshort bones,.
Understanding the Context
Bone Age Assessment is essential for monitoring physiologic and abnormal growth of children and adolescents, allowing the identification of skeletal maturation.
Abstract Bone Age Assessment is essential for monitoring physiologic and abnormal growth of children and adolescents, allowing the identification of skeletal maturation.
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.
