在肩膀MRI上旋转手套撕裂的患者级分类使用可解释的视觉转换器框架
Murat Aşçı1, Sergen Aşık2,3, Ahmet Yazıcı3,4
1Department of Orthopedics and Traumatology, Faculty of Medicine, Bilecik Şeyh Edebali University, Bilecik 11230, Türkiye.
Journal of clinical medicine
|February 13, 2026
概括
本研究介绍了患者意识视觉变压器 (Pa-ViT) 用于使用MRI诊断旋转手套撕裂 (RCT). 可解释的AI模型达到91%的准确性,改善了部分厚度撕裂的检测.
科学领域:
- 整形外科 整形外科 整形外科
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 通过MRI诊断旋转手套撕裂 (RCT) 是具有挑战性的,因为复杂的解剖学和观察者之间的变化.
- 以切片为中心的卷积神经网络 (CNN) 对于精确的RCT分级而言,与体积上下文作斗争.
- 这项研究解决了改进自动化RCT分类的需求.
研究的目的:
- 开发和验证患者意识视觉转换器 (Pa-ViT) 以实现自动化,患者一级的RCT分类.
- 提高诊断灵敏度,特别是微妙的部分厚度撕裂.
- 为临床决策支持提供可解释的深度学习框架.
主要方法:
- 使用了2447个T2加权冠状肩部MRI扫描的回顾性数据集.
- 在弱监督多实例学习 (MIL) 范式中使用视觉转换器 (ViT-Base) 骨干.
- 用加权交叉损失训练模型,并与CNN和传统分类器进行基准测试.
主要成果:
- Pa-ViT实现了91%的整体准确率和0.91的宏观平均F1得分,超过了VGG-16 (87%).
- 在部分厚度撕裂方面表现出卓越的性能 (ROC AUC: 0.903).
- 注意推出可视化证实依赖于解剖特征,而不是文物.
结论:
- Pa-ViT有效地模拟了远程依赖,为RCT诊断提供了CNN的强有力的替代方案.
- 该框架提供了一个临床上可行的,可解释的工具,用于提高诊断灵敏度.
- Pa-ViT显示了提高微妙的部分厚度旋转手套撕裂的检测的承诺.
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