深度学习诊断和严重程度分层的间歇性肺部疾病和慢性阻塞性肺部疾病在数字肺部听觉和超声波:观察病例控制研究的临床方案
Johan N Siebert1,2, Mary-Anne Hartley3, Delphine S Courvoisier4,5
1Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1211, Geneva 14, Switzerland. Johan.Siebert@hcuge.ch.
BMC pulmonary medicine
|June 1, 2023
概括
人工智能现在可以通过肺声和超声波更早地帮助诊断肺部疾病,如异常性肺纤维化 (IPF) 和慢性阻塞性肺病 (COPD). 这种人工智能方法旨在通过更快,更准确的疾病检测来改善患者护理.
科学领域:
- 肺部医学 肺部医学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 间歇性肺部疾病 (ILD) 和慢性阻塞性肺部疾病 (COPD) 是严重的,进展性肺部疾病,预后不佳.
- 早期和准确的诊断对于及时干预,疾病进展管理和改善生存率至关重要.
- 传统的诊断方法可能是主观的,并且取决于操作者.
研究的目的:
- 评估人工智能辅助肺部听觉和超声波 (LUS) 的潜力,以更早,更准确地诊断ILD和COPD.
- 开发一种深度学习模型,用于IPF,NSIP和COPD的自动诊断和严重程度分层.
- 探索自动肺部疾病检测点护理测试的协同价值.
主要方法:
- 一个标准化采集数字获取的肺声和LUS图像的协议,来自IPF,NSIP或COPD的成人门诊患者,以及年龄匹配的对照.
- 在瑞士肺病诊所招募120名患者 (IPF,NSIP,COPD) 和40名对照患者.
- 使用卷积神经网络,LSTM和转换器对音频和LUS数据进行深度学习算法 (DeepBreath) 的训练,用于自动诊断.
主要成果:
- 主要结果是准确诊断ILD与对照或COPD.
- 二次结果包括描述IPF,NSIP和COPD的临床,功能和放射性特征.
- 该研究的目标是实现ROC曲线下的面积大于80%以区分正常和病态的肺声音.
结论:
- 人工智能辅助肺部听觉和LUS显示出作为ILD和COPD客观,准确和早期诊断工具的潜力.
- 这种方法可以通过实现自动检测,差异诊断和严重程度估计来指导护理管理.
- 该研究协议概述了一种标准化的数据收集和AI模型开发方法.
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