Articlehybrid2021

Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment

Kyu-Chong Lee · Kee‐Hyoung Lee · Chang Ho Kang · Kyung‐Sik Ahn · Lindsey Yoojin Chung · Jae-Joon Lee +3 more

38 citations0/0 files storedKorean Journal of Radiology

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Forensic Anthropology and Bioarchaeology Studies Artificial Intelligence in Healthcare and Education Medical Imaging and Analysis

Abstract

OBJECTIVE: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. MATERIALS AND METHODS: test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. RESULTS: < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). CONCLUSION: The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.

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References

1959RADIOGRAPHIC ATLAS OF SKELETAL DEVELOPMENT OF THE HAND AND WRIST 2017Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs 2017Fully Automated Deep Learning System for Bone Age Assessment 2009The BoneXpert Method for Automated Determination of Skeletal Maturity 2016Deep learning for automated skeletal bone age assessment in X-ray images 2001Assessment of skeletal maturity and prediction of adult height:(TW3 Method)1999Bone age assessment: a large scale comparison of the Greulich and Pyle, and Tanner and Whitehouse (TW2) methods 1996Bone age in children of diverse ethnicity.

Cited by

2023Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels 2023Prediction of Fishman’s skeletal maturity indicators using artificial intelligence 2023Use of artificial intelligence in determination of bone age of the healthy individuals: A scoping review 2022Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children 2024

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