지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한소아내분비학회 Annals of Pediatirc Endocrinology & Metabolism Annals of Pediatric Endocrinology & Metabolism Vol.29 No.2
- 발행연도
- 2024.4
- 수록면
- 102 - 108 (7page)
- DOI
- 10.6065/apem.2346050.025
이용수
초록· 키워드
오류제보하기
Purpose: Bone age (BA) is needed to assess developmental status and growth disorders. We evaluated the clinical performance of a deep-learning-based BA software to estimate the chronological age (CA) of healthy Korean children.Methods: This retrospective study included 371 healthy children (217 boys, 154 girls), aged between 4 and 17 years, who visited the Department of Pediatrics for health check-ups between January 2017 and December 2018. A total of 553 left-hand radiographs from 371 healthy Korean children were evaluated using a commercial deep-learning-based BA software (BoneAge, Vuno, Seoul, Korea). The clinical performance of the deep learning (DL) software was determined using the concordance rate and Bland-Altman analysis via comparison with the CA.Results: A 2-sample <i>t</i>-test (<i>P</i><0.001) and Fisher exact test (<i>P</i>=0.011) showed a significant difference between the normal CA and the BA estimated by the DL software. There was good correlation between the 2 variables (<i>r</i>=0.96, <i>P</i><0.001); however, the root mean square error was 15.4 months. With a 12-month cutoff, the concordance rate was 58.8%. The Bland-Altman plot showed that the DL software tended to underestimate the BA compared with the CA, especially in children under the age of 8.3 years.Conclusion: The DL-based BA software showed a low concordance rate and a tendency to underestimate the BA in healthy Korean children.
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