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KMID : 1036920240290020102
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Annals of Pediatric Endocrinology & Metabolism
2024 Volume.29 No. 2 p.102 ~ p.108
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Clinical validation of a deep-learning-based bone age software in healthy Korean children
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Nam Hyo-Kyoung
Lea Wu-In Winnah
Yang Ze-Pa
Noh Eun-Jin
Rhie Young-Jun
Lee Kee-Hyoung
Hong Suk-Joo
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Abstract
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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 t-test (P<0.001) and Fisher exact test (P=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 (r=0.96, P<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.
Conclusions: 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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KEYWORD
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Age determination by skeleton, Child, Child health, Deep learning, Software
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