一个基于深度学习的放射性分类器用于通常的间歇性肺炎
Jonathan H Chung1, Lydia Chelala1, Janelle Vu Pugashetti2
1Department of Radiology, University of Chicago, Chicago, IL.
Chest
|October 16, 2023
概括
常见间歇性肺炎 (UIP) 的深度学习 (DL) 分类器在胸部CT扫描上识别UIP时显示出高准确度. 这种自动化工具可以有效地查患者,并识别患有间歇性肺病 (ILD) 的高风险个人.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 胸部CT扫描是诊断间歇性肺病 (ILD) 的主要工具,取代了手术肺活检.
- 对CT扫描的标准化解释对于准确的ILD诊断至关重要.
- 深度学习 (DL) 为自动化和标准化CT扫描分析提供了潜力.
研究的目的:
- 开发和评估一个基于DL的常见间歇性肺炎 (UIP) 的分类器,使用胸部CT特征.
- 评估DL分类器在分辨放射科医生确定的视觉UIP时的准确性.
- 为了比较基于DL的生存歧视和视觉UIP分类.
主要方法:
- 对来自不同数据源的2,907张胸部CT扫描进行了回顾性研究.
- 一个在CT特征上训练的卷积神经网络,以预测UIP概率.
- 用于分类的线性支向量机器,在独立队列中评估性能.
- 使用卡普兰-梅尔和考克斯回归分析的无移植生存率.
主要成果:
- 基于DL的UIP分类器在性能队列中实现了93%的灵敏度和86%的特异性.
- 在多中心ILD队列中,分类器显示了81%的灵敏度和77%的特异性.
- 基于DL和视觉UIP的分类表明存在类似的生存歧视.
- 对于基于DL的UIP分类,无论视觉分类如何,结果都是一致的.
结论:
- 基于DL的UIP分类器在不同的流行率中显示出强大的表现.
- 该自动化工具准确地识别了胸部CT扫描上的UIP,与专家放射科医生相似.
- 这个DL分类器可以有效地选UIP,并识别高风险ILD表型.
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