通过深度学习检测慢性阻塞性肺病,使用吸气和呼气胸部计算机断层扫描和临床信息
Zhuoneng Zhang1, Fan Wu2,3, Yumin Zhou2,3
1School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China.
Journal of thoracic disease
|October 24, 2024
概括
一个新的卷积神经网络 (CNN) 模型使用双相CT扫描和临床数据准确检测慢性阻塞性肺病 (COPD). 这种人工智能工具为诊断COPD提供了一个有前途的解决方案,改善了患者的治疗结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 尽管有医疗评估,但慢性阻塞性肺病 (COPD) 患者未被诊断的比例正在增加.
- 需要改进诊断工具,以便早期和准确地检测COPD.
- 当前诊断方法在识别所有受影响个体方面的局限性.
研究的目的:
- 为自动化COPD检测开发一种新的卷积神经网络 (CNN) 模型.
- 利用双相 (吸气和呼气) 胸部计算机断层扫描 (CT) 图像和临床数据来提高诊断准确度.
- 将拟议的CNN模型与其他方法的性能进行比较.
主要方法:
- 开发一个CNN模型,集成一个剩余特征提取块网络 (RFEBNet) 用于CT图像分析和一个完全连接的前网络 (FCNet) 用于临床数据.
- 培训和验证使用来自三家医院的2,047名参与者的前性数据.
- 对比分析包括RFEBNet与双/单相CT图像和FCNet仅提供临床信息.
主要成果:
- 拟议的CNN模型在内部测试集上实现了0.930的接收器运行特征曲线 (AUC) 下的高面积.
- 性能仅使用CT图像 (AUC从0.888到0.912) 或临床数据 (AUC = 0.805) 的模型.
- 该模型在外部测试组中表现出强大的性能,AUC为0.896.
结论:
- 开发的CNN模型有效地检测COPD使用双相CT图像和临床信息的组合.
- 该模型具有很高的准确性,并有可能用于COPD自动诊断.
- 这种由人工智能驱动的方法在COPD检测策略方面取得了重大进展.
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