通过使用人工智能从schlieren图像预测呼吸速率
Byung Jun Kim1, Woo Sang Cho2, Hyungsoo Lim1
1Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul 08826, , Republic of Korea; Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul 03080, Republic of Korea.
这项研究引入了一种新的,非侵入性呼吸频率监测系统,使用斯克莱伦成像和人工智能 (AI). 这种由人工智能驱动的系统准确地评估呼吸模式,为目前的方法提供了稳定的替代方案.
科学领域:
- 生理监测是指对身体进行生理监测.
- 医学成像医学成像
- 医疗保健中的人工智能
背景情况:
- 呼吸速率是患者评估的关键重要标志.
- 目前用于测量呼吸速率的方法可能是侵入性的或不稳定的.
- 需要非侵入性和稳定的呼吸系统监测解决方案.
研究的目的:
- 开发一种非侵入性和稳定的呼吸速率测量方法.
- 为了利用schlieren成像和人工智能 (AI) 进行呼吸监测.
- 评估AI模型在评估呼吸速率方面的表现.
主要方法:
- 开发了一个结合schlieren成像与AI的系统,以可视化空气流和评估呼吸.
- 采集了40名健康受试者在不同呼吸速率下采集的脑膜图像.
- 训练并比较了三个人工智能模型 (ResNet,InceptionNet,EfficientNet) 用于呼吸分类和信号重建.
主要成果:
- 在人工智能模型中,EfficientNet获得了最高的精度 (0.92).
- 使用EfficientNet.Net观察到高精度 (0.90),灵敏度 (0.93) 和F1得分 (0.92).
- 预测和实际呼吸速率之间有很好的类内相关性 (0.98-0.99).
结论:
- 基于人工智能的斯克莱伦成像系统为准确的呼吸评估提供了稳定且非侵入性的方法.
- 这项技术有可能改善患者监测和临床决策.
- 需要在不同的临床环境中进一步验证.
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