利用放射学和可解释的人工智能对常见和非特异性间歇性肺炎进行分类
Turkey Refaee1, Ouf Aloofy2, Khalid Alduraibi2
1Department of Diagnostic Radiography Technology, Faculty of Nursing and Health Sciences, Jazan University, Jazan 85145, Saudi Arabia.
Journal of clinical medicine
|July 29, 2025
概括
高分辨率计算机断层扫描 (HRCT) 扫描的放射性分析显著改善了与单独临床模型相比,通常间歇性肺炎 (UIP) 和非特异性间歇性肺炎 (NSIP) 之间的准确差异化.
科学领域:
- 肺部医学 肺部医学
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 准确区分常见间歇性肺炎 (UIP) 和非特异性间歇性肺炎 (NSIP) 对于有效的间歇性肺病 (ILD) 管理至关重要.
- 当前的诊断方法在区分这些疾病方面可能存在局限性.
研究的目的:
- 用HRCT扫描来评估临床,放射性和组合模型在分类UIP与NSIP中的诊断性能.
- 评估放射性特征在提高分类准确性方面的实用性.
主要方法:
- 对105个HRCT扫描 (60个UIP,45个NSIP) 的追溯分析.
- 使用pyRadiomics开发临床 (人口统计,肺功能) 和放射性模型.
- 通过递归特征消除和使用AUC的模型性能评估来选择特征.
- 使用夏普利添加式解释 (SHAP) 解释模型预测.
主要成果:
- 临床模型的AUC达到0.62.2.
- 放射性模型表现出优异的性能,AUC为0.90 (灵敏度>85%,特异性>85%).
- 综合模型的AUC值为0.86.6.
- SHAP分析强调了纹理特征 (GLCM_Idmn,NGTDM_Contrast) 作为关键分类器.
结论:
- 与单独的临床数据相比,放射性特征显著提高了分类UIP和NSIP的准确性.
- 将放射学纳入临床工作流程具有降低诊断变异性和改善ILD诊断的潜力.
- 建议使用更大,多中心数据集进行进一步验证.
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