开发和验证混合深度学习-机器学习方法,用于评估COVID-19和其他肺炎的严重程度
Doohyun Park1, Ryoungwoo Jang2, Myung Jin Chung3,4
1School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
Scientific reports
|August 17, 2023
概括
一个新的混合DDM模型有效地使用CT扫描来评估传染性呼吸道疾病的严重程度. 这种在COVID-19数据上训练的AI工具,显示出对诊断流感和其他病毒性肺炎的前景.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 传染性疾病 传染性疾病
背景情况:
- 冠状病毒疾病2019 (COVID-19) 大流行突出了传染性呼吸道疾病 (IRD) 的持续威胁.
- 预测和评估IRD的严重程度对于有效的患者管理和公共卫生反应至关重要.
- 目前的方法可能不足以对各种IRD进行快速准确的严重性评估.
研究的目的:
- 为IRD开发和验证一个强大的严重性评估模型,包括COVID-19,流感和新型流感.
- 通过CT成像数据利用深度学习和机器学习技术,提高诊断准确度.
- 创建一个适用于不同类型病毒性肺炎的多功能模型.
主要方法:
- 开发了一种混合深度学习和机器学习模型 (Hybrid-DDM).
- 该模型在649名COVID-19患者的CT图像上进行了训练,并在156名COVID-19患者内部验证.
- 在多中心数据集上进行了外部验证,包括1138名COVID-19患者和233名流感/新型流感患者.
主要成果:
- 混合DDM模型在所有验证数据集中表现出与基线模型相比的优异性能.
- 混合DDM的接收器运行曲线 (AUC) 下的面积为0.830 (D1),0.801 (D2) 和0.774 (D3),明显超过了基线.
- 该模型显示,在内部和外部验证集中,准确度显著提高 (p <0.05).
结论:
- 在COVID-19数据上训练的混合DDM模型有效地使用CT图像评估IRD严重程度.
- 该模型显示了可用于各种病毒性肺炎,包括流感的重大潜力.
- 这种人工智能驱动的方法为在后疫情时代管理传染性呼吸道疾病提供了有希望的工具.
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