Jia-Wei Tang , Fen Li , Xin Liu , Jin-Ting Wang , Xue-Song Xiong , Xiang-Yu Lu , Xin-Yu Zhang , Yu-Ting Si , Zeeshan Umar , Alfred Chin Yen Tay , Barry J. Marshall , Wei-Xuan Yang , Bing Gu , Liang Wang
{"title":"通过表面增强拉曼光谱和机器学习算法检测人胃液中的幽门螺旋杆菌感染。","authors":"Jia-Wei Tang , Fen Li , Xin Liu , Jin-Ting Wang , Xue-Song Xiong , Xiang-Yu Lu , Xin-Yu Zhang , Yu-Ting Si , Zeeshan Umar , Alfred Chin Yen Tay , Barry J. Marshall , Wei-Xuan Yang , Bing Gu , Liang Wang","doi":"10.1016/j.labinv.2023.100310","DOIUrl":null,"url":null,"abstract":"<div><p>Diagnostic methods for <span><span>Helicobacter pylori</span></span><span><span> infection include, but are not limited to, urea breath test, serum </span>antibody test<span>, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for </span></span><em>H. pylori</em> diagnosis. In this study, we propose a novel technique for accurately detecting <em>H. pylori</em> infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (<em>n</em> = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown <em>H. pylori</em> infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing <em>H. pylori</em> infection, potentially complementing current <em>H. pylori</em> diagnostic methods.</p></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms\",\"authors\":\"Jia-Wei Tang , Fen Li , Xin Liu , Jin-Ting Wang , Xue-Song Xiong , Xiang-Yu Lu , Xin-Yu Zhang , Yu-Ting Si , Zeeshan Umar , Alfred Chin Yen Tay , Barry J. Marshall , Wei-Xuan Yang , Bing Gu , Liang Wang\",\"doi\":\"10.1016/j.labinv.2023.100310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Diagnostic methods for <span><span>Helicobacter pylori</span></span><span><span> infection include, but are not limited to, urea breath test, serum </span>antibody test<span>, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for </span></span><em>H. pylori</em> diagnosis. In this study, we propose a novel technique for accurately detecting <em>H. pylori</em> infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (<em>n</em> = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown <em>H. pylori</em> infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing <em>H. pylori</em> infection, potentially complementing current <em>H. pylori</em> diagnostic methods.</p></div>\",\"PeriodicalId\":17930,\"journal\":{\"name\":\"Laboratory Investigation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2023-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Laboratory Investigation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0023683723002532\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Laboratory Investigation","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0023683723002532","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms
Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.
期刊介绍:
Laboratory Investigation is an international journal owned by the United States and Canadian Academy of Pathology. Laboratory Investigation offers prompt publication of high-quality original research in all biomedical disciplines relating to the understanding of human disease and the application of new methods to the diagnosis of disease. Both human and experimental studies are welcome.