Clinical validation of a deep-learning-based bone age software in healthy Korean children
| Autor: | Hyo-Kyoung Nam, Winnah Wu-In Lea, Zepa Yang, Eunjin Noh, Young-Jun Rhie, Kee-Hyoung Lee, Suk-Joo Hong |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Annals of Pediatric Endocrinology & Metabolism, Vol 29, Iss 2, Pp 102-108 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2287-1012 2287-1292 |
| DOI: | 10.6065/apem.2346050.025 |
| Popis: | 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 |
| Databáze: | Directory of Open Access Journals |
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