骨龄估计和预测最终的成年人身高使用深度学习
Junghwan Suh1, Jinkyoung Heo2, Su Jin Kim1
1Department of Pediatrics, Severance Children's Hospital, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Korea.
Yonsei medical journal
|October 26, 2023
概括
一个新的人工智能 (AI) 程序准确地估计了骨年龄,并预测了儿童的最终成年身高. 这种深度学习工具有助于评估儿科内分泌学的生长状态.
科学领域:
- 儿科内分泌学 儿科内分泌学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 准确评估儿童生长状况依赖于身高评估和骨年龄估计.
- 传统的方法需要专家对放射图进行解释.
研究的目的:
- 开发一个基于深度学习的程序来估计骨年龄.
- 建立一个模型来预测韩国儿童的最终成年人身高.
主要方法:
- 一个卷积神经网络深度学习模型被训练和验证,使用来自866名儿童的1678张X线图.
- 该模型的性能与参考骨龄和人类评分器相比,使用一组150张X射线图的测试组进行了评估.
- 用100名成年人的数据评估了最终的成年人身高预测.
主要成果:
- 人工智能计划显示,与参考骨年龄有很强的相关性 (r=0.99,p<0.001).
- 骨年龄估计的平均绝对误差 (MAE) 为0.59年,根平均平方误差 (RMSE) 为0.55年,相当于经验丰富的儿科内分泌学家.
- 人工智能程序预测了最终的成年人身高,MAE为4.62厘米.
结论:
- 成功开发了一种用于骨龄估计的新型深度学习程序.
- 人工智能工具在估计骨龄和预测儿科患者最终成年人身高方面表现出很高的准确性.
- 这项技术为儿科内分泌学中生长评估提供了有前途的进展.
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