使用深度学习从手部射线图确定骨年龄
Joshua T Bram1, Ayoosh Pareek2, Samuel A Beber1
1Lerner Children's Pavilion, Hospital for Special Surgery, New York, New York, USA.
The American journal of sports medicine
|August 15, 2025
概括
这项研究开发了一种深度学习 (DL) 模型,可以从手部放射图中准确估计骨年龄,显著减少估计时间. 该模型的性能优于以前的方法,帮助外科医生为年轻患者制定治疗计划.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 整形外科 整形外科 整形外科
背景情况:
- 骨年龄估计对于儿科患者的手术规划至关重要.
- 使用手部放射图的传统方法是耗时的.
研究的目的:
- 开发准确可靠的深度学习 (DL) 模型,以从手部X射线图中确定骨年龄.
主要方法:
- 使用了三个公共数据集 (RSNA,RHPE,DHA),包括手部放射,性别和骨年龄.
- 训练了一个ConvNeXt模型,在第二次代中结合了时间年龄.
- 在包括机构数据集在内的各种数据集上验证的模型.
主要成果:
- 在RSNA测试组中,DL模型实现了3.68个月的平均绝对误差 (MAE).
- 模型的性能超过了以前的最先进的方法,显示出显著的改善 (例如,DHA上的19.8%).
结论:
- 开发了一种强大的DL模型,用于使用超过20,000张手部X射线图来预测骨龄.
- 该模型为临床使用提供了一个高度准确和可靠的平台,改善了手术选择和节省时间.
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