轻度卷积神经网络检测慢性阻塞性肺病 (COPDxNet):一个多中心模型开发和外部验证研究.
Akm Shahariar Azad Rabby1,2, Muhammad F A Chaudhary1,3, Pratim Saha1,2
1Center for Lung Analytics and Imaging Research (CLAIR), The University of Alabama at Birmingham, Birmingham, AL, 35294.
medRxiv : the preprint server for health sciences
|August 6, 2025
概括
一个新的深度学习模型,COPDxNet,可以使用胸部CT扫描精确检测慢性阻塞性肺病 (COPD). 这种工具对改善成年人COPD的机会性查和诊断充满希望.
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:
- 慢性阻塞性肺病 (COPD) 影响了很大一部分成年人,许多人仍未被诊断出来.
- 通过胸部计算机断层扫描 (CT) 进行机会性查,为提高COPD检测率提供了一个可行的策略.
- 深度学习模型为开发简单,临床适用的COPD识别工具提供了一条途径.
研究的目的:
- 开发和验证一种深度学习模型,用于使用最小处理的胸部CT扫描来检测COPD.
- 评估模型在标准剂量和低剂量CT扫描中的性能.
- 评估模型在不同患者群体和成像协议中的通用性.
主要方法:
- 开发了一个轻量级的卷积神经网络,COPDxNet.
- 该模型接受了COPDGene研究中的13,043张胸部CT扫描的训练,随机分为训练和测试集.
- 对来自SPIROMICS和国家肺部查试验 (NLST) 的数据集进行了外部验证.
主要成果:
- 在COPDGene队列中,COPDxNet在标准剂量CT扫描 (AUC 0.92) 和低剂量扫描 (AUC 0.88) 上检测了COPD的高精度.
- 该模型在外部验证数据集上表现出强的性能,SPIROMICS的AUC为0.92,NLST为0.82.
- 该模型在所有数据集中得到了良好的校准,表明了可靠的概率估计.
结论:
- COPDxNet在各种胸部CT扫描类型上对COPD检测具有很高的分辨准确性和概括性.
- 该模型的性能支持其在临床实践和对不同人群的查计划中的潜在实用性.
- 这种深度学习方法可以帮助识别未被诊断的COPD病例,改善患者的治疗结果.
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