References

• 2017Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs456 cited
• 2016Deep learning for automated skeletal bone age assessment in X-ray images434 cited
• 2018Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph134 cited
• 2018MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling90 cited
• 2021Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment65 cited
• 2015Assessment of Bone Age in Prepubertal Healthy Korean Children: Comparison among the Korean Standard Bone Age Chart, Greulich-Pyle Method, and Tanner-Whitehouse Method56 cited
• 2019A multi-scale data fusion framework for bone age assessment with convolutional neural networks52 cited
• 2021Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment51 cited
• 2021Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment38 cited
• 2019Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists37 cited
• 2022Bone age assessment and sex determination using transfer learning35 cited
• 2018A quantitative method for the radiological assessment of skeletal maturity using the distal femur31 cited
• 2020Self-Supervised Attention Mechanism for Pediatric Bone Age Assessment With Efficient Weak Annotation