热成像和基于深度学习的适合性检查用于呼吸道保护
Hyunjin Kim1,2, Tong Min Kim2, Sae Won Choi3
1Department of Medical Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
Scientific reports
|October 17, 2024
概括
这项研究介绍了一种使用热成像检查面具实时匹配的AI模型. 3DCNN模型准确地识别了各种口罩类型的正确口罩佩戴,提高了公共卫生安全.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 公共卫生 公共卫生
背景情况:
- 适当佩戴口罩对于预防疾病传播至关重要.
- 实时评估面具的适合性仍然是一个挑战.
- 热成像为检测空气泄漏提供了一种新的方法.
研究的目的:
- 开发一个人工智能模型,使用热视频实时评估面具的适合性.
- 评估不同深度学习模型对此任务的性能.
- 为了确定热成像在识别正确的口罩使用中的有效性.
主要方法:
- 从50名参与者中收集了5000个热视频,他们穿着五种类型的口罩,无论是错误的还是正确的.
- 利用深度学习模型,特别是3D卷积神经网络 (3DCNN) 和卷积长期短期记忆 (ConvLSTM).
- 通过使用接收器运行特征曲线下的区域 (AUROC) 评估模型性能,用于二进制和多重分类任务.
主要成果:
- 3DCNN模型在面具佩戴方法的二进制和多种分类方面表现优于ConvLSTM.
- 使用3DCNN模型实现的最高AUROC为0.986的多重分类.
- 所有的口罩类型都实现了大于0.9的AUROC值,其中KF-AD口罩是最好的分类.
结论:
- 由人工智能驱动的热成像提供了一种有效和准确的实时口罩适合检查方法.
- 开发的模型可以在各种口罩类型中推广,为公众和职业健康提供了显著的优势.
- 这项技术可以提高高风险环境中的安全性,提高医疗保健系统的效率.
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