一个设备不变的多模式学习框架,用于呼吸系统疾病分类
Mo Yang1, Xuefei Liu2, Wei Du2
1Research&Development Department, Luca Healthcare, Shanghai, China.
NPJ digital medicine
|February 26, 2026
概括
这项研究引入了一个新的AI框架,用于基于智能手机的呼吸道疾病查,使用咳声音,人口统计和症状. 这种方法可以提高COPD等疾病的不同设备的诊断准确性.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 信号处理 信号处理
背景情况:
- 基于智能手机的咳分析显示了远程呼吸系统疾病查的前景.
- 现有的深度学习模型面临的局限性是由于设备的可变性,人口的多样性,以及多式联网数据集成的挑战.
研究的目的:
- 开发一个设备不变的,多式联络深度学习框架,以使用咳声学,人口统计和症状准确地对成人呼吸系统疾病进行多标签分类.
- 增强人工智能模型的稳定性和通用性,用于基于咳的呼吸道诊断.
主要方法:
- 提出了一个多式联网深度学习框架,其中包含了一个对抗分支,用于设备不变的音频特征学习.
- 雇员不变风险最小化-增加损失以提高对非结构性转变的稳定性.
- 利用一个现实世界的多中心数据集,包括7种呼吸系统疾病的1万多例病例.
主要成果:
- 在识别慢性阻塞性肺病 (COPD) (AUROC 0.9698),下呼吸道感染 (LRTI) (AUROC 0.8483) 和肺阴影 (PS) (AUROC 0.8720) 中取得了卓越的表现.
- 在识别7种呼吸道疾病的并发症方面取得了有希望的结果 (总体AUROC 0.8907).
- 有效地减轻了设备效应,并改善了基于咳的诊断的跨设备概括性.
结论:
- 开发的AI框架为咳驱动的呼吸道查提供了一个可扩展和可转移的方法.
- 多模式融合和强大的表示学习对于推进AI在呼吸道诊断中的临床适用性至关重要.
- 该方法显示了在家庭环境中自我管理护理的巨大潜力.
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