电子鼻
肝硬化
可穿戴计算机
混合氧化物燃料
灵敏度(控制系统)
医学
计算机科学
材料科学
内科学
电子工程
人工智能
工程类
嵌入式系统
冶金
铀
作者
Andreas Voss,Rico Schroeder,Steffen Schulz,Jens Haueisen,Stefanie Vogler,Paul Horn,Andreas Stallmach,Philipp A. Reuken
出处
期刊:Biosensors
[Multidisciplinary Digital Publishing Institute]
日期:2022-01-26
卷期号:12 (2): 70-70
被引量:25
摘要
The purpose of this exploratory study was to determine whether liver dysfunction can be generally classified using a wearable electronic nose based on semiconductor metal oxide (MOx) gas sensors, and whether the extent of this dysfunction can be quantified. MOx gas sensors are attractive because of their simplicity, high sensitivity, low cost, and stability. A total of 30 participants were enrolled, 10 of them being healthy controls, 10 with compensated cirrhosis, and 10 with decompensated cirrhosis. We used three sensor modules with a total of nine different MOx layers to detect reducible, easily oxidizable, and highly oxidizable gases. The complex data analysis in the time and non-linear dynamics domains is based on the extraction of 10 features from the sensor time series of the extracted breathing gas measurement cycles. The sensitivity, specificity, and accuracy for distinguishing compensated and decompensated cirrhosis patients from healthy controls was 1.00. Patients with compensated and decompensated cirrhosis could be separated with a sensitivity of 0.90 (correctly classified decompensated cirrhosis), a specificity of 1.00 (correctly classified compensated cirrhosis), and an accuracy of 0.95. Our wearable, non-invasive system provides a promising tool to detect liver dysfunctions on a functional basis. Therefore, it could provide valuable support in preoperative examinations or for initial diagnosis by the general practitioner, as it provides non-invasive, rapid, and cost-effective analysis results.
科研通智能强力驱动
Strongly Powered by AbleSci AI