你只听一次的小说 (YOLO) 深度学习模型用于自动突出的肠道声音检测:健康受试者的可行性研究
Rohan Kalahasty1, Gayathri Yerrapragada1, Jieun Lee1
1Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究开发了一种人工智能模型,使用声肠图 (PEG) 检测出突出的肠声 (BSs),为胃肠道疾病诊断提供一种非侵入性方法. 人工智能模型实现了高精度,为未来的诊断工具铺平了道路.
科学领域:
- 胃肠病学 胃肠病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 准确的胃肠道疾病诊断通常依赖于具有风险的侵入性手术.
- 对于肠道声音 (BSs) 的传统评估是主观的,缺乏诊断价值.
- 声内图 (PEG) 提供BS的非侵入性声学分析,但需要用于临床实用性的增强.
研究的目的:
- 开发一个名为You Only Listen Once (YOLO) 的自动化人工智能 (AI) 模型,用于检测突出的肠道声音.
- 建立一种非侵入性,成本效益高的胃肠道疾病诊断模式,使用人工智能增强的PEG.
- 为了实现肠道声音的实时分析,以用于未来的胃肠道疾病检测和诊断.
主要方法:
- 从健康的志愿者中使用Eko DUO耳语镜记录了110个2分钟的PEG.
- 医生的注释BS录音,将它们分类为突出的或模糊的.
- 开发和训练了一种1D-CNN增强式变压器模型,使用Mel频率的CEPSTRAL系数进行BS分类.
主要成果:
- 人工智能模型实现了接收器运行曲线 (ROC) 下的面积为0.92.
- 该模型表现出高性能,精度为86.6%,精度为86.85%,回忆率为86.08%.
- YOLO模型成功地分类了突出的肠道声音,表明其对胃肠道疾病分析的潜力.
结论:
- 使用AI和PEG的自动化肠道声音检测显示出作为一种非侵入性诊断工具的重大前景.
- YOLO模型的性能验证了人工智能增强的胃肠道疾病声学分析的潜力.
- 需要对多样化的队列进行进一步的研究,以确认其对临床应用的稳定性和通用性.
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