诊断和COPD的严重程度评估基于机器学习的胸部CT图像
He Sui1, Zhanhao Mo1, Ying Wei2
1China-Japan Union Hospital of Jilin University, Changchun, People's Republic of China.
International journal of chronic obstructive pulmonary disease
|August 20, 2025
概括
这项研究开发了一个机器学习模型,使用胸部CT扫描来诊断和分级慢性阻塞性肺病 (COPD). 人工智能模型准确预测肺功能,有助于COPD诊断和严重程度评估.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 肺部病理学 肺部病理学
背景情况:
- 慢性阻塞性肺病 (COPD) 患者在急性阶段的肺功能测试可能很困难.
- 需要自动诊断工具来提高效率和准确性.
研究的目的:
- 开发一种机器学习模型,利用胸部CT图像进行自动化COPD诊断和分级.
- 为了提高COPD评估的诊断效率和准确性.
主要方法:
- 对173名COPD患者和176名对照人群进行了回顾性研究.
- 深度学习用于CT图像上的肺膜,呼吸道和血管系统的自动细分.
- 使用支持矢量机 (SVM) 分类器进行特征提取,选择 (曼-惠特尼U测试,LASSO) 和模型开发.
主要成果:
- 图像模型实现了高AUC (0.981培训,0.977测试) 和准确性 (0.949培训,0.956测试) 的COPD诊断.
- 对于COPD严重程度分级,该模型获得AUC值为0.889 (训练) 和0.796 (测试),准确度分别为0.784和0.719.
- 该模型在内部和外部测试套件上表现出强的性能.
结论:
- 使用胸部CT图像的机器学习模型可以准确诊断COPD.
- 这些人工智能模型可以有效地评估COPD的严重程度.
- 开发的模型显示了潜在的帮助临床医生在COPD诊断和管理.
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