深度学习类别的风湿性关节炎在手的X射线图解释性见解和Web应用程序
Kanglin Cai1,2, Dengfeng Dou3, Guibing Deng4
1The Second People's Hospital Affiliated to Three Gorges University / Yichang Second People's Hospital, Yichang, Hubei, 443000, People's Republic of China.
ImmunoTargets and therapy
|November 25, 2025
概括
本研究引入了一种可解释的深度学习模型,用于手部X射线中检测类风湿性关节炎 (RA),实现高精度并为临床使用提供视觉解释.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 类风湿病学 类风湿病学
背景情况:
- 类风湿性关节炎 (RA) 诊断依赖于放射性评估,通常是主观的.
- 手部放射图的自动分析可以提高诊断的准确性和效率.
- 深度学习为客观和快速的RA分类提供了潜力.
研究的目的:
- 开发一个可解释的深度学习框架,用于手部X射线图中的类型化类型化类型化类型化类型化类型化类型化类型化类型化类型化类型化.
- 阐明深度学习模型的决策模式.
- 通过基于Web的部署来实现临床翻译.
主要方法:
- 一项回顾性研究分析了1655张RA患者和健康对照者的手部X射线图.
- 一个视觉几何组 (VGG) -8卷积神经网络被训练在增强的X射线图像上.
- 使用梯度加权类激活映射 (Grad-CAM) 和沙普利增量解释 (SHAP) 评估可解释性.
- 为了临床实用性,使用Streamlit开发了一个Web应用程序.
主要成果:
- 分类器实现了高训练性能 (AUC=0.99,精度=0.94) 和可概括的测试指标 (AUC=0.81,精度=0.74).
- 解释性分析强调了关节空间狭窄,骨质侵蚀和椎变化作为关键的病理指标.
- 在SHAP分析中,甲手腕和手腕关节被确定为关键预测特征.
- 该网络应用程序在协助手部X射线的RA诊断方面表现出实用性.
结论:
- 开发的框架将深度学习与可解释的洞察力结合起来,用于手部X射线扫描中的RA诊断.
- 这种方法有助于早期发现RA,并提高临床医生的能力.
- 该模型提供了对RA放射性标记物的诊断援助和教育见解.
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