Shepherd S F, McGuire N D, de Lacy Costello B P J, Ewen R J, Jayasena D H, Vaughan K, Ahmed I, Probert C S, Ratcliffe N M
Institute of Bio-sensing Technology, University of the West of England, Bristol BS16 1QY, UK.
J Breath Res. 2014 Jun;8(2):026001. doi: 10.1088/1752-7155/8/2/026001. Epub 2014 Mar 27.
There is much clinical interest in the development of a low-cost and reliable test for diagnosing inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS), two very distinct diseases that can present with similar symptoms. The assessment of stool samples for the diagnosis of gastro-intestinal diseases is in principle an ideal non-invasive testing method. This paper presents an approach to stool analysis using headspace gas chromatography and a single metal oxide sensor coupled to artificial neural network software. Currently, the system is able to distinguish samples from patients with IBS from patients with IBD with a sensitivity and specificity of 76% and 88% respectively, with an overall mean predictive accuracy of 76%.
开发一种低成本且可靠的测试方法来诊断炎症性肠病(IBD)和肠易激综合征(IBS)引起了临床的广泛关注,这两种截然不同的疾病却可能表现出相似的症状。通过评估粪便样本诊断胃肠疾病原则上是一种理想的非侵入性检测方法。本文介绍了一种利用顶空气相色谱法和与人工神经网络软件相结合的单一金属氧化物传感器进行粪便分析的方法。目前,该系统能够区分IBS患者和IBD患者的样本,灵敏度和特异性分别为76%和88%,总体平均预测准确率为76%。