Sang Brian, Wen Haoran, Junek Greg, Neveu Wendy, Di Francesco Lorenzo, Romberg Justin, Ayazi Farrokh
Georgia Institute of Technology, Atlanta, GA, 30308, USA.
StethX Inc., Atlanta, GA, 30308, USA.
Sci Rep. 2025 Mar 15;15(1):9015. doi: 10.1038/s41598-025-93011-7.
Physicians evaluate a patient's respiratory health during a physical examination by visual assessment of the work of breathing (WoB) to determine respiratory stability, and by detecting abnormal lung sounds via lung auscultation using a stethoscope to identify common pathological lung diseases, such as chronic obstructive pulmonary disease (COPD) and pneumonia. Since these assessment methods are subjective, a low-profile device used for an accurate and quantitative monitoring approach could provide valuable preemptive insights into respiratory health, proving to be clinically beneficial. To achieve this goal, we have developed a miniature patch consisting of a sensitive wideband multi-axis seismometer that can be placed on the anatomical areas of a patient's lungs to enable an effective quantification of a patient's WoB and lung sounds. When used on a patch, the seismometer captures chest wall vibrations due to respiratory muscle effort, known as high-frequency mechanomyogram (MMG), during tidal breathing as well as seismic pulmonary-induced vibrations (PIVs) during deep breathing due to normal and/or adventitious lung sounds like crackles, while simultaneously recording respiration rate and phase. A system comprised of multiple patches was evaluated on 124 patients in the hospital setting and shown to accurately assess and quantify a patent's physical signs of WoB by measuring the average respiratory effort extracted from high-frequency MMG signals, demonstrating statistical significance of this method in comparison to clinical bedside observation of WoB and respiration rate. A data fusion deep learning model was developed which combined the inputs of PIVs lung sounds and the corresponding respiration phase to detect crackle, wheeze and normal breath sound features. The model exhibited high accuracy, sensitivity, specificity, precision and F1 score of 93%, 93%, 97%, 93% and 93% respectively, with area under the curve (AUC) of precision recall (PR) of 0.97 on the test set. Additionally, the PIVs with corresponding respiration phase captured from each auscultation point generated an acoustic map of the patient's lung, which correlated with traditional lung radiographic findings.
在体格检查期间，医生通过视觉评估呼吸功（WoB）来判断呼吸稳定性，从而评估患者的呼吸健康状况；同时，借助听诊器进行肺部听诊，检测异常肺音，以识别常见的肺部病理性疾病，如慢性阻塞性肺疾病（COPD）和肺炎。由于这些评估方法具有主观性，一种用于精确和定量监测的低调设备能够为呼吸健康提供有价值的前瞻性见解，在临床上被证明是有益的。为实现这一目标，我们开发了一种微型贴片，它由一个灵敏的宽带多轴地震计组成，可以放置在患者肺部的解剖区域，以便有效地量化患者的呼吸功和肺音。当贴片使用时，地震计在潮气呼吸期间捕捉由于呼吸肌努力引起的胸壁振动，即高频肌动图（MMG），以及在深呼吸期间由于正常和/或诸如湿啰音等附加肺音引起的肺部诱发地震振动（PIVs），同时记录呼吸频率和相位。在医院环境中，对124名患者使用由多个贴片组成的系统进行评估，结果表明，通过测量从高频MMG信号中提取的平均呼吸努力，可以准确评估和量化患者的呼吸功体征，与床边对呼吸功和呼吸频率的临床观察相比，该方法具有统计学意义。开发了一种数据融合深度学习模型，该模型结合了PIVs肺音和相应呼吸相位的输入，以检测湿啰音、哮鸣音和正常呼吸音特征。该模型在测试集上的准确率、灵敏度、特异性、精确率和F1分数分别为93%、93%、97%、93%和93%，精确召回率（PR）曲线下面积（AUC）为0.97。此外，从每个听诊点捕获的具有相应呼吸相位的PIVs生成了患者肺部的声学图，这与传统的肺部X光检查结果相关。
