基于深度学习的慢性阻塞性肺病恶化预测使用流量和体积时间曲线成像:回顾性队列研究
Eun-Tae Jeon1, Heemoon Park2, Jung-Kyu Lee2
1Department of Neurology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea.
Journal of medical Internet research
|May 15, 2025
概括
一个整合临床数据和螺旋计图像的AI模型准确地预测了COPD (AE-COPD) 的急性恶化. 这种方法增强了对高风险个体的早期识别,以便主动管理.
科学领域:
- 肺部医学 肺部医学
- 医疗保健中的人工智能
- 预测分析是一种预测分析.
背景情况:
- 慢性阻塞性肺病 (COPD) 是一种渐进的呼吸道疾病,急性恶化 (AE-COPD) 引起的显著发病率.
- 传统的螺旋测量指标捕获有限的生理数据,低估了AE-COPD风险.
- 人工智能 (AI) 提供完整的螺旋计曲线的高级分析,以识别表明恶化风险的复杂模式.
研究的目的:
- 为了评估是否一个AI增强的预测模型整合临床数据和螺旋测量图像提高AE-COPD预测准确性.
- 将AI-PFT-Clin模型与仅用于预测中度至严重和严重AE-COPD事件的临床模型进行比较.
主要方法:
- 使用注册数据对10,492例COPD病例 (2004-2020年) 的回顾性队列研究.
- 开发了一个AI-PFT-Clin模型,将临床变量和螺旋测量图像 (流量-体积,体积-时间曲线) 结合起来.
- 将AI-PFT-Clin模型与Clin模型 (仅临床变量) 的性能进行比较,用于在一年内预测AE-COPD事件.
主要成果:
- 在外部验证队列中,AI-PFT-Clin模型显示出优异的预测准确性 (中度至重度AE-COPD的AUC为0.755与0.730;严重AE-COPD的0.713与0.675).
- 更高的AI-PFT-Clin得分与AE-COPD风险增加显著相关 (aHR 4.21).
- 该模型在10年的随访期间显示了持续的预测稳定性和各子组的可靠表现.
结论:
- 使用人工智能将临床数据与螺旋计图像集成,可以显著提高AE-COPD预测的准确性,而不是仅仅使用临床数据.
- AI-PFT-Clin模型通过检测微妙的生理异常来促进高风险个体的早期识别.
- 这种基于人工智能的方法具有积极主动的COPD管理和个性化干预策略的潜力.
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