Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Korea.
Healthcare Readiness Institute for Unified Korea, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Korea.
Eur Radiol. 2024 Oct;34(10):6396-6406. doi: 10.1007/s00330-024-10748-x. Epub 2024 Apr 27.
To improve pubertal bone age (BA) evaluation by developing a precise and practical elbow BA classification using the olecranon, and a deep-learning AI model.
Lateral elbow radiographs taken for BA evaluation in children under 18 years were collected from January 2020 to June 2022, retrospectively. A novel classification and the olecranon BA were established based on the morphological changes in the olecranon ossification process during puberty. The olecranon BA was compared with other elbow and hand BA methods, using intraclass correlation coefficients (ICCs), and a deep-learning AI model was developed.
A total of 3508 lateral elbow radiographs (mean age 9.8 ± 1.8 years) were collected. The olecranon BA showed the highest applicability (100%) and interobserver agreement (ICC 0.993) among elbow BA methods. It showed excellent reliability with Sauvegrain (0.967 in girls, 0.969 in boys) and Dimeglio (0.978 in girls, 0.978 in boys) elbow BA methods, as well as Korean standard (KS) hand BA in boys (0.917), and good reliability with KS in girls (0.896) and Greulich-Pyle (GP)/Tanner-Whitehouse (TW)3 (0.835 in girls, 0.895 in boys) hand BA methods. The AI model for olecranon BA showed an accuracy of 0.96 and a specificity of 0.98 with EfficientDet-b4. External validation showed an accuracy of 0.86 and a specificity of 0.91.
The olecranon BA evaluation for puberty, requiring only a lateral elbow radiograph, showed the highest applicability and interobserver agreement, and excellent reliability with other BA evaluation methods, along with a high performance of the AI model.
This AI model uses a single lateral elbow radiograph to determine bone age for puberty from the olecranon ossification center and can improve pubertal bone age assessment with the highest applicability and excellent reliability compared to previous methods.
Elbow bone age is valuable for pubertal bone age assessment, but conventional methods have limitations. Olecranon bone age and its AI model showed high performances for pubertal bone age assessment. Olecranon bone age system is practical and accurate while requiring only a single lateral elbow radiograph.
通过开发一种精确实用的肘骨龄分类方法，利用鹰嘴骨来评估青春期骨龄，并建立深度学习人工智能模型。
回顾性收集 2020 年 1 月至 2022 年 6 月期间因青春期骨龄评估而拍摄的 18 岁以下儿童的肘部外侧 X 光片。基于鹰嘴骨在青春期骨化过程中的形态变化，建立了一种新的分类和鹰嘴骨龄。通过使用组内相关系数（ICC）比较了鹰嘴骨龄与其他肘部和手部骨龄方法，并开发了深度学习人工智能模型。
共收集 3508 张肘部外侧 X 光片（平均年龄 9.8±1.8 岁）。鹰嘴骨龄在肘部骨龄方法中具有最高的适用性（100%）和观察者间一致性（ICC 0.993）。它与 Sauvegrain（女孩 0.967，男孩 0.969）和 Dimeglio（女孩 0.978，男孩 0.978）肘部骨龄方法以及男孩的韩国标准（KS）手部骨龄（0.917）具有极好的可靠性，与女孩的 KS（0.896）和 Greulich-Pyle（GP）/Tanner-Whitehouse（TW）3（女孩 0.835，男孩 0.895）手部骨龄方法具有良好的可靠性。鹰嘴骨龄的人工智能模型的准确性为 0.96，特异性为 0.98，使用的是 EfficientDet-b4。外部验证的准确性为 0.86，特异性为 0.91。
仅需一张肘部外侧 X 光片即可评估青春期鹰嘴骨龄，具有最高的适用性和观察者间一致性，与其他骨龄评估方法具有极好的可靠性，并且人工智能模型的性能也很高。
该人工智能模型使用单个肘部外侧 X 光片从鹰嘴骨化中心确定青春期的骨龄，与之前的方法相比，具有最高的适用性和极好的可靠性，可以提高青春期骨龄评估的准确性。
肘部骨龄对青春期骨龄评估很有价值，但传统方法存在局限性。鹰嘴骨龄及其人工智能模型在青春期骨龄评估中表现出很高的性能。鹰嘴骨龄系统实用准确，仅需一张肘部外侧 X 光片。
