Articlegold2023

Bone Age Estimation and Prediction of Final Adult Height Using Deep Learning

Junghwan Suh · JinKyoung Heo · Sujin Kim · Soyeong Park · Mo Kyung Jung · Han Saem Choi +8 more

21 citations0/0 files storedYonsei Medical Journal

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Forensic Anthropology and Bioarchaeology Studies Artificial Intelligence in Healthcare and Education Growth Hormone and Insulin-like Growth Factors

Abstract

PURPOSE: The appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to predict the final adult height of Korean children. MATERIALS AND METHODS: A total of 1678 radiographs from 866 children, for which the interpretation results were consistent between two pediatric endocrinologists, were used to train and validate the deep learning model. The bone age estimation algorithm was based on the convolutional neural network of the deep learning system. The test set simulation was performed by a deep learning program and two raters using 150 radiographs and final height data for 100 adults. RESULTS: <0.001). In the test set simulation, the AI program showed a mean absolute error (MAE) of 0.59 years and a root mean squared error (RMSE) of 0.55 years, compared with reference bone age, and showed similar accuracy to that of an experienced pediatric endocrinologist (rater 1). Prediction of final adult height by the AI program showed an MAE of 4.62 cm, compared with the actual final adult height. CONCLUSION: We developed a bone age estimation program based on a deep learning algorithm. The AI-derived program demonstrated high accuracy in estimating bone age and predicting the final adult height of Korean children and adolescents.

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References

1977The Measurement of Observer Agreement for Categorical Data 1959RADIOGRAPHIC ATLAS OF SKELETAL DEVELOPMENT OF THE HAND AND WRIST 1976Assessment of Skeletal Maturity and Prediction of Adult Height (TW2 Method)2017Fully Automated Deep Learning System for Bone Age Assessment 2017Computerized Bone Age Estimation Using Deep Learning Based Program: Evaluation of the Accuracy and Efficiency 2019Incorporated region detection and classification using deep convolutional networks for bone age assessment 2019TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks 2020Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method

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2025Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence 2025M-CNN-RF: A hybrid deep learning model for accurate pediatric skeletal age estimation using hand bone radiographs 2025Artificial Intelligence-Based Models for Automated Bone Age Assessment from Posteroanterior Wrist X-Rays: A Systematic Review 2025Automated Bone Age Assessment and Adult Height Prediction from Pediatric Hand Radiographs via a Cascaded Deep Learning Framework

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