Clinical validation of a deep-learning-based bone age software on healthy Korean children.

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Abstract

Bone age is needed to assess developmental status and growth disorders. We aimed to evaluate the clinical performance of a deep learning-based bone age software on the chronological age of healthy Korean children.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 of 371 healthy Korean children were evaluated using a commercial deep learning-based bone age software (BoneAge, Vuno, Seoul, Korea). The clinical performance of the deep learning software was determined using the concordance rate and Bland-Altman analysis via comparison with the chronological age.A two-sample t-test (P < 0.001) and Fisher’s exact test (P = 0.011) showed a significant difference between the normal chronological age and the bone age estimated by the deep learning software. There was a good correlation between the two variables (r = 0.96, P < 0.001); however, the root mean square error was 15.4 months. With a 12-month cut-off, the concordance rate was 58.8%. The Bland-Altman plot showed that the deep learning software tended to underestimate the bone age compared with the chronological age, especially in children under the age of 8.3 years.The deep learning-based bone age software showed a low concordance rate and a tendency to underestimate the bone age in healthy Korean children.