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KMID : 1024020200500030237
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Imaging Science in Dentistry
2020 Volume.50 No. 3 p.237 ~ p.243
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Evaluation of the clinical efficacy of a TW3-based fully automated bone age assessment system using deep neural networks
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Shin Nan-Young
Lee Byoung-Dai
Kang Ju-Hee
Kim Hye-Rin
Oh Dong-Hyo
Lee Byung-Il
Kim Sung-Hyun
Lee Mu-Sook
Heo Min-Suk
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Abstract
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Purpose: The aim of this study was to evaluate the clinical efficacy of a Tanner-Whitehouse 3 (TW3)-based fully automated bone age assessment system on hand-wrist radiographs of Korean children and adolescents.
Materials and Methods: Hand-wrist radiographs of 80 subjects (40 boys and 40 girls, 7?15 years of age) were collected. The clinical efficacy was evaluated by comparing the bone ages that were determined using the system with those from the reference standard produced by 2 oral and maxillofacial radiologists. Comparisons were conducted using the paired t-test and simple regression analysis.
Results: The bone ages estimated with this bone age assessment system were not significantly different from those obtained with the reference standard (P>0.05) and satisfied the equivalence criterion of 0.6 years within the 95% confidence interval (? 0.07 to 0.22), demonstrating excellent performance of the system. Similarly, in the comparisons of gender subgroups, no significant difference in bone age between the values produced by the system and the reference standard was observed (P>0.05 for both boys and girls). The determination coefficients obtained via regression analysis were 0.962, 0.945, and 0.952 for boys, girls, and overall, respectively (P=0.000); hence, the radiologist-determined bone ages and the system-determined bone ages were strongly correlated.
Conclusion: This TW3-based system can be effectively used for bone age assessment based on hand-wrist radiographs of Korean children and adolescents.
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KEYWORD
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Age Determination by Skeleton, Radiography, Deep Learning, Artificial Intelligence
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