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Evaluation of the clinical efficacy of a TW3-based fully automated bone age assessment system using deep neural networks

Imaging Science in Dentistry 2020년 50권 3호 p.237 ~ 243
신난영, 이병대, 강주희, 김혜린, 오동효, 이병일, 김성현, 이무숙, 허민석,
소속 상세정보
신난영 ( Shin Nan-Young ) - Seoul National University School of Dentistry Department of Oral and Maxillofacial Radiology
이병대 ( Lee Byoung-Dai ) - HealthHub Center for Artificial Intelligence in Medicine and Imaging
강주희 ( Kang Ju-Hee ) - Seoul National University School of Dentistry Department of Oral and Maxillofacial Radiology
김혜린 ( Kim Hye-Rin ) - Seoul National University School of Dentistry Department of Oral and Maxillofacial Radiology
오동효 ( Oh Dong-Hyo ) - HealthHub Center for Artificial Intelligence in Medicine and Imaging
이병일 ( Lee Byung-Il ) - HealthHub Center for Artificial Intelligence in Medicine and Imaging
김성현 ( Kim Sung-Hyun ) - HealthHub Center for Artificial Intelligence in Medicine and Imaging
이무숙 ( Lee Mu-Sook ) - Keimyung University Dongsan Hospital Department of Radiology
허민석 ( Heo Min-Suk ) - Seoul National University School of Dentistry Department of Oral and Maxillofacial Radiology

Abstract


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.

키워드

Age Determination by Skeleton; Radiography; Deep Learning; Artificial Intelligence

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