生理信息驱动的数字听诊用于长期护理居民肺炎检测
Physiologically Informed Digital Auscultation for Pneumonia Detection in Long-term Care Residents
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- University of Washington（华盛顿大学）
- University of Washington Bothell（华盛顿大学博塞尔分校）
- Virufy (The Covid Detection Foundation)（Virufy（新冠检测基金会））
- University of Tsukuba（筑波大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本研究利用多通道数字听诊和深度学习，以X光监督训练CNN模型，在长期护理居民中实现高效肺炎检测，优于临床监督并减少通道数。
中文摘要 AI 辅助
肺炎在老年长期护理居民中难以诊断；多病共存和非典型表现掩盖了体征，这促使需要操作高效的客观检测方法。我们分析了来自185名日本居民（73名肺炎患者，112名有症状但非肺炎患者）的多通道数字听诊器录音，使用放射科医生确认的胸部X光片和临床医生诊断作为监督信号，训练卷积神经网络、多模态融合及基于通道的变体，并采用时域Grad-CAM可解释性。模型通过重复的患者级交叉验证进行评估，结果显示，使用X光监督的模型优于临床监督（F1 0.729，准确率0.783，对比F1 0.637，准确率0.711）。此外，三通道选择协议保持了性能（F1 0.736；准确率0.803），其中两个中胸部位排名最高，且Grad-CAM注意力与附加呼吸音重叠。这些发现表明，自动化多通道肺音分析可辅助长期护理中的肺炎诊断，其中X光监督比临床监督更可靠，且较少的通道在降低采集时间的同时保持了性能。
英文摘要
Pneumonia is difficult to diagnose in older long-term care residents; multimorbidity and atypical presentations obscure signs, motivating operationally efficient objective testing. We analyzed multi-channel digital stethoscope recordings from 185 Japanese residents (73 pneumonia, 112 symptomatic without), using radiologist-confirmed chest X-rays and clinician diagnoses as supervisory signals that train convolutional neural networks, multimodal fusion, and channel-based variants with time-domain Grad-CAM interpretability. Models were evaluated with repeated patient-level cross-validation showing models with X-ray supervision outperformed clinician supervision (F1 0.729, accuracy 0.783 vs. F1 0.637, accuracy 0.711). Additionally, a three-channel selection protocol maintained performance (F1 0.736; accuracy 0.803), with two mid-thoracic sites ranking highest and Grad-CAM attention overlapping adventitious sounds. These findings indicate automated multi-channel lung-sound analysis can aid long-term care pneumonia diagnosis, with X-ray supervision being more reliable than clinical, and fewer channels preserving performance while lowering acquisition times.
