使用可穿戴式肠道声音事件发现的自动炎症性肠病检测
Annalisa Baronetto1,2, Sarah Fischer3,4, Markus F Neurath3,4
1Hahn-Schickard, Freiburg, Germany.
Frontiers in digital health
|April 4, 2025
概括
这项研究使用智能T恤来检测炎症性肠病 (IBD) 患者的异常肠声 (BS). 开发的算法在区分IBD患者和健康个体方面取得了高精度,支持潜在的诊断应用.
科学领域:
- 胃肠病学 胃肠病学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 炎症性肠道疾病 (IBD) 可以改变肠道声音 (BS) 的特征.
- 传统的听觉可能会错过微妙的BS事件,表明IBD.
- 带有麦克风的智能T恤为持续的腹部声音监测提供了一种新的方法.
研究的目的:
- 开发和验证用于检测罕见肠道声音事件的模式发现算法.
- 调查自动BS检测在分类IBD患者与健康对照中的临床相关性.
- 评估基于BS的分类与IBD疾病活动相关的性能.
主要方法:
- 从24名IBD患者和21名健康对照组中收集了连续的腹部音频录音.
- 基于深度学习的算法被训练为从大约281小时的数据中自动检索肠道声音事件.
- 在检测到的BS事件周围提取的特征被用来训练一个梯度增强分类器用于患者分类.
主要成果:
- BS检测算法实现了接受器运行特征曲线 (AUC) 下的平均面积≥0.83.3.
- 无论使用手动注释的BS还是算法检测的BS,分类性能都是可比的.
- 对分类窗口大小和功能相关性的探索为模型的行为提供了洞察力.
结论:
- 使用模式发现算法自动检索肠道声音显示了支持IBD诊断的巨大潜力.
- 这项技术提供了一种非侵入性方法来监测并可能有助于治疗IBD患者.
- 进一步开发可以将BS分析纳入IBD评估的常规临床实践.
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