ArticleScientific reports2022
External validation of deep learning-based bone-age software: a preliminary study with real world data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial intelligence algorithm improves radiologists' bone age assessment accuracy.Journal of the Chinese Medical Association : JCMA · 2025Trial
- Quantifying human variability in pediatric bone age assessment: a multi-institutional multi-reader study and benchmark for AI evaluation.Pediatric radiology · 2026Article
- External Validation of Deeplasia for Automated Bone Age Assessment Compared with Four Commercial AI Systems.Diagnostics (Basel, Switzerland) · 2026Article
- Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.Pediatric radiology · 2026Review
- [Artificial intelligence algorithm for bone age estimation].Revista medica del Instituto Mexicano del Seguro Social · 2026Observational
- Population-specific calibration and validation of an open-source bone age AI.Scientific reports · 2025Article
- Potential of Artificial Intelligence for Bone Age Assessment in Iranian Children and Adolescents: An Exploratory Study.Archives of Iranian medicine · 2025Article
- Review
- Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels.Korean journal of radiology · 2023Article
- Bone Age Estimation and Prediction of Final Adult Height Using Deep Learning.Yonsei medical journal · 2023Article
- Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children.BMC pediatrics · 2022Article
- Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
6 authors.
Funding
Abstract
Artificial intelligence (AI) is increasingly being used in bone-age (BA) assessment due to its complicated and lengthy nature. We aimed to evaluate the clinical performance of a commercially available deep learning (DL)-based software for BA assessment using a real-world data. From Nov. 2018 to Feb. 2019, 474 children (35 boys, 439 girls, age 4-17 years) were enrolled. We compared the BA estimated by DL software (DL-BA) with that independently estimated by 3 reviewers (R1: Musculoskeletal radiologist, R2: Radiology resident, R3: Pediatric endocrinologist) using the traditional Greulich-Pyle atlas, then to his/her chronological age (CA). A paired t-test, Pearson's correlation coefficient, Bland-Altman plot, mean absolute error (MAE) and root mean square error (RMSE) were used for the statistical analysis. The intraclass correlation coefficient (ICC) was used for inter-rater variation. There were significant differences between DL-BA and each reviewer's BA (P < 0.025), but the correlation was good with one another (r = 0.983, P < 0.025). RMSE (MAE) values were 10.09 (7.21), 10.76 (7.88) and 13.06 (10.06) months between DL-BA and R1, R2, R3 BA. Compared with the CA, RMSE (MAE) values were 13.54 (11.06), 15.18 (12.11), 16.19 (12.78) and 19.53 (17.71) months for DL-BA, R1, R2, R3 BA, respectively. Bland-Altman plots revealed the software and reviewers' tendency to overestimate the BA in general. ICC values between 3 reviewers were 0.97, 0.85 and 0.86, and the overall ICC value was 0.93. The BA estimated by DL-based software showed statistically similar, or even better performance than that of reviewers' compared to the chronological age in the real world clinic.
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