通过胸部X射线使慢性阻塞性肺病诊断成为可能:一个多地点和多模式的研究
Ryan Wang1, Li-Ching Chen1, Lama Moukheiber2
1Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.
International journal of medical informatics
|September 10, 2023
概括
深度学习模型可以使用胸部X射线 (CXR) 检测慢性阻塞性肺病 (COPD),为早期诊断提供潜在的查工具,特别是在螺旋计较难获得的资源有限地区.
科学领域:
- 肺部医学 肺部医学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 慢性阻塞性肺病 (COPD) 是一个普遍存在的全球呼吸道疾病.
- 早期诊断COPD是具有挑战性的,往往导致延迟或无效的治疗.
- 螺旋计,目前的诊断标准,面临着可访问性问题,特别是在资源较差的环境中.
研究的目的:
- 开发和验证用于使用胸部X射线 (CXRs) 检测COPD的深度学习 (DL) 模型.
- 探索CXRs作为查工具的潜力,用于识别可能患有COPD的患者.
- 评估不同人口群体的模型性能,以确保公平性.
主要方法:
- 使用了三个CXR数据集和相关的电子健康记录 (EHR).
- 开发了两个DL融合方案:模型级 (Bootstrap聚合) 和数据级 (多模式CXR和EHR数据).
- 进行了公平性分析,以评估人口统计学子组的模型性能.
主要成果:
- 深度学习模型在检测CXRs的COPD时,实现了曲线下的面积 (AUC) 大于0.75.
- 开发的模型有望促进COPD患者查.
- CXRs为COPD查提供了一种可行的替代方案,在那些对螺旋计的访问有限的地区.
结论:
- 胸部X射线是一种广泛可用的诊断测试,可用于早期发现COPD.
- 进一步的研究可以建立在这些发现的基础上,以改善COPD的早期诊断和治疗.
- 这种方法有可能改变许多患者的疾病进程,特别是那些未被诊断或治疗不足的患者.
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