基于X射线图像的双阶段卷积变压器网络的自动骨骨龄评估
Xiongwei Mao1,2, Qinglei Hui3, Siyu Zhu4
1Department of Radiology, Zhejiang University Hospital, Zhejiang University, Hangzhou 310027, China.
Diagnostics (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一个由人工智能驱动的两级卷积变压器网络,用于准确的骨年龄评估 (BAA). 深度学习模型显著减少错误和临床工作量,使得快速,自动和精确的评估.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 骨龄评估 (BAA) 对于评估骨发育和成熟度至关重要.
- 目前的临床BAA方法耗时,主观,缺乏一致性.
- 深度学习通过从手部X射线图中提取复杂的特征来显示BAA的前景.
研究的目的:
- 开发一个更准确,更一致,更有效的自动化BAA系统.
- 解决传统BAA方法和现有的深度学习方法的局限性.
- 通过专注于特定的手骨区域来提高BAA的精度.
主要方法:
- 为BAA提出了一个两级卷积变压器网络.
- 第一个阶段使用YOLOv5进行感兴趣区域 (ROI) 提取和手骨姿势对齐.
- 生物性别信息被整合到变压器中,并在第二阶段使用窗口注意力机制来提取特征.
主要成果:
- 拟议的方法在验证组中达到6.22个月的平均绝对误差 (MAE),在测试组中达到4.585个月.
- 在6个月和12个月的累计准确性分别达到71%和96%.
- 该系统表现出与最先进的方法相当的性能,显著减少了临床工作量.
结论:
- 开发的两级卷积变压器网络为骨年龄评估提供了快速,自动和高精度的解决方案.
- 该方法有效地从特定的手骨ROI中提取相关特征,提高准确性和一致性.
- 这种人工智能驱动的方法有可能显著减少与BAA相关的临床工作量.
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