一个低成本的人工智能驱动的呼吸分析仪,用于在资源有限的环境中早期检测慢性阻塞性肺病
Evans Kumi1, Tendai T Machaya1, Justice K Appati2
1Department of Computer Science and Information Systems, Ashesi University, Berekuso, Ghana.
Digital health
|March 9, 2026
概括
一个新的人工智能 (AI) 驱动的基于呼吸的诊断 (BBD) 系统使用呼出的化合物提供了准确,低成本的慢性阻塞性肺病 (COPD) 检测. 这种创新技术可以提高资源有限的环境中的可访问性.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 慢性阻塞性肺病 (COPD) 在低收入和中等收入国家被严重低诊断.
- 目前的诊断方法,如螺旋计,是昂贵的,需要广泛的基础设施.
- 迫切需要可访问的,COPD的实时诊断工具.
研究的目的:
- 设计和验证一个低成本,人工智能驱动的基于呼吸的诊断 (BBD) 系统,用于实时检测COPD.
- 使用呼出的挥发性有机化合物 (VOC) 进行非侵入性诊断.
- 开发一个适合资源有限的医疗保健机构的系统.
主要方法:
- 将金属氧化物半导体传感器与轻量级卷积神经网络集成在树Pi 5上,用于边缘AI.
- 使用公开和定制的VOC数据集进行评估,并对数据进行增强,以提高对传感器漂移和环境变化的稳定性.
- 开发一个多语言的移动界面和Retrieval-Augmented Generation聊天机器人,用于患者参与,确保HIPAA和FHIR合规.
主要成果:
- 该BBD系统实现了96.68%的准确性和100%的精度用于COPD检测.
- 低推理延迟 (0.02毫秒) 和功耗 (<2.5W) 已被证明.
- 五个传感器配置保持了98%的性能,同时降低了30%的成本;高可用性 (SUS得分86.3/100) 和聊天机器人信任 (4.4/5) 得到证实.
结论:
- 该研究证实了可解释,低成本,节能BBD系统的可行性,用于早期检测COPD.
- 该系统利用边缘人工智能和负担得起的传感器,使其适合资源有限的环境.
- 这种BBD系统提供了一个可扩展的解决方案,用于整合到全球呼吸系统健康战略中.
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