使用深度学习在肺结核患者中区分感染性
Yi Gao1,2,3, Yiwen Zhang4, Chengguang Hu3
1Department of Infectious Disease and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Frontiers in public health
|December 13, 2023
概括
一个新的深度学习模型,TBINet,可以使用CT扫描来识别传染性肺结核 (PTB) 患者. 这种方法在区分PTB病例中的传染性水平方面表现有前途.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染性疾病 传染性疾病
背景情况:
- 肺结核 (PTB) 仍然是一个重大的全球卫生挑战.
- 准确评估PTB传染性对于有效的疾病控制至关重要.
- 目前用于评估PTB传染性的方法可能耗时且资源密集.
研究的目的:
- 开发和评估一个深度学习模型,TBINet,用于区分PTB患者的感染性,使用计算机断层扫描 (CT) 图像.
- 将TBINet的性能与传统的深度学习模型进行比较.
- 为了提高深度学习模型预测的可解释性.
主要方法:
- 来自四个中心的925名PTB患者的数据集根据唾液涂抹结果进行了策划和标记弱和强感染性.
- 在这个数据集上训练了TBINet模型,并将其性能与3D ResNet模型进行了比较.
- 梯度加权类激活映射 (Grad-CAM) 用于模型可解释性,以识别CT图像中的病变激活部位.
主要成果:
- TBINet实现了卓越的性能,在验证组件上,曲线下的面积 (AUC) 为0.819,在外部测试组件上为0.753.
- 格拉德-CAM分析表明,CT特征如巩固,空洞,上叶参与和扩大的淋巴结与较高的PTB感染性相关.
- 与现有的深度学习方法相比,该模型在识别传染病患者方面表现出更好的准确性.
结论:
- 该研究表明,利用CT图像和深度学习来评估PTB感染性的可行性.
- TBINet提供了一个有希望的,非侵入性的工具,用于识别传染性PTB患者.
- 这种方法可能有助于PTB的早期发现和管理策略.
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