多模式深度学习与解剖学上受限的注意力用于选MRI检测到的TMJ异常从全景图像
Hyo-Jung Jung1,2, Dayun Ju3, Chanyoung Kim3
1Department of Orofacial Pain and Oral Medicine, College of Dentistry, Yonsei University, Seoul, Republic of Korea.
NPJ digital medicine
|January 23, 2026
概括
开发一种用于关节疾病的AI查工具有助于早期诊断. 这种深度学习框架分析了X射线图和临床数据,以识别TMJ异常,改善MRI转诊患者的分组.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 矯正牙科 矯正牙科是一種矯正牙科.
背景情况:
- 部疾病 (TMD) 的早期诊断是困难的.
- 关节内关节 (TMJ) 异常需要磁共振成像 (MRI) 来确认.
研究的目的:
- 开发一种全面的查方法,用于MRI检测到的TMJ病理.
- 为TMJ异常检测创建可解释的深度学习框架.
主要方法:
- 开发了一个深度学习框架,使用配对的开口和闭口TMJ全景放射和临床元数据.
- 综合的解剖学指导注意力,多模式的临床特征,以及集体学习.
- 使用梯度加权类激活映射用于可视化和可解释性.
主要成果:
- 性能最好的整体框架实现了0.86.6的曲线下的面积.
- 证明了MRI阴性和阳性病例的平衡分类.
- 确认了对状区域的关注,并显示了临床元数据和空间注意力的附加值.
结论:
- 原型工作流可以有效地对患者进行MRI转诊.
- 支持早期检测TMJ异常.
- 促进对关节疾病的及时干预.
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