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Population-specific calibration and validation of an open-source bone age AI
Sebastian Rassmann1,2, Luka Abashishvili3, Elene Melikidze3
1Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany.
Scientific Reports
|September 23, 2025
Summary
Artificial intelligence (AI) for bone age (BA) assessment was adapted for Georgian children. The calibrated AI model, Deeplasia-GE, significantly improved accuracy, offering a reliable tool for pediatric growth monitoring in this population.
Area of Science:
- Pediatric endocrinology
- Medical imaging analysis
- Artificial intelligence in healthcare
Background:
- Accurate bone age (BA) assessment is vital for monitoring pediatric growth and guiding treatments like hormonal therapy.
- Existing artificial intelligence (AI) models for BA assessment often lack generalizability due to training on limited, non-diverse datasets.
- Population-specific variations in bone growth necessitate tailored AI solutions for accurate skeletal maturity evaluation.
Purpose of the Study:
- To adapt and validate an open-source AI tool (Deeplasia) for bone age assessment in the Georgian pediatric population.
- To address concerns regarding the applicability of existing AI BA models to underrepresented ancestries.
- To improve the accuracy and reliability of automated BA assessment for Georgian children.
Main Methods:
- Retrospective collection of 381 pediatric hand X-rays from the Georgian population.
- Establishment of a manual bone age reference rating by seven local pediatric specialists.
- Sex-specific linear calibration of the Deeplasia AI model using 121 images, creating Deeplasia-GE.
- Evaluation of model performance on a held-out test set of 260 images.
Main Results:
- The default Deeplasia model showed a mean absolute difference (MAD) of 6.57 months, which improved to 5.69 months after calibration (Deeplasia-GE).
- Initial overestimation of BA by the default model (SMD of +2.85 months for females, +5.35 months for males) was significantly reduced by Deeplasia-GE (-0.03 months for females, +0.58 months for males).
- Deeplasia-GE demonstrated a smaller error than individual human raters and maintained high test-retest reliability.
Conclusions:
- The calibrated AI model, Deeplasia-GE, provides a reliable and accurate method for bone age assessment in Georgian children.
- This study highlights the importance of population-specific calibration for AI-driven medical diagnostic tools.
- Deeplasia-GE offers a valuable advancement for pediatric growth monitoring and treatment guidance within the Georgian healthcare system.
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