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基于超级微血管成像的可解释深度学习模型,用于慢性脏疾病中间歇性纤维化的非侵入性诊断

Xiachuan Qin¹, Xiaoling Liu², Weihan Xiao³

¹Department of Ultrasound, Chengdu Second People's Hospital, Chengdu, Sichuan 610000, China (X.Q.); Department of Ultrasound, The first affiliated hospital of Anhui Medical University, Hefei, Anhui 230022, China (X.Q., X.L., Q.L., L.X., C.Z.).

Academic radiology

|December 17, 2024

概括

使用卓越的微血管成像 (SMI) 的新可解释深度学习 (XDL) 模型,在慢性病 (CKD) 患者中准确诊断间歇性纤维化 (IF),非侵入性,优于传统的超声波方法.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
医疗成像医学成像
人工智能的人工智能

背景情况:

慢性病 (CKD) 的诊断通常依赖于侵入性方法.
间歇性纤维化 (IF) 是CKD进展的关键指标.
对于准确的IF评估,需要使用非侵入性成像技术.

研究的目的:

开发一个可解释的深度学习 (XDL) 模型,使用卓越的微血管成像 (SMI).
为了在CKD患者中实现间歇性纤维化 (IF) 程度的非侵入性诊断.
为了比较XDL-SMI模型与超声波放射学和CDUS的性能.

主要方法:

包括365名接受脏活检,超声波和SMI的CKD患者.
开发了一个基于SMI的XDL模型,使用超声波放射学和CDUS作为控制.
使用接收器操作特征 (ROC) 曲线和决策曲线分析评估模型效用.

主要成果:

在验证 (AUC 0.854) 和测试 (AUC 0.824) 两组中,XDL-SMI模型显示出卓越的诊断准确性.
在XDL-SMI中,超声波放射学 (验证AUC为0.784,测试AUC为0.792) 和CDUS (验证AUC为0.745,测试AUC为0.752) 的表现优于XDL-SMI.
沙普利添加式解释 (SHAP) 通过曲面图和热图提供了可解释性.

结论:

基于SMI的DL模型为CKD的非侵入性IF程度判断提供了更高的准确性.
XDL-SMI模型为传统超声波方法提供了更精确,更可靠的替代方案.
基于SHAP的可视化增强了模型的解释性,突出显示了IF诊断关键的图像区域.

关键词:

慢性脏疾病慢性脏疾病可以解释的深度学习.间歇性纤维化间歇性纤维化这是一种非侵入性的方法.优秀的微血管成像技术

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