首页|Northwest University Reports Findings in Artificial Intelligence (Vaginal microb iota molecular profiling and diagnostic performance of artificial intelligence-a ssisted multiplex PCR testing in women with bacterial vaginosis: a single-center ...)
Northwest University Reports Findings in Artificial Intelligence (Vaginal microb iota molecular profiling and diagnostic performance of artificial intelligence-a ssisted multiplex PCR testing in women with bacterial vaginosis: a single-center ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Xi’an , People’s Republic of China, by NewsRx correspondents, research stated, “Bacter ial vaginosis (BV) is a most common microbiological syndrome. The use of molecul ar methods, such as multiplex real-time PCR (mPCR) and next-generation sequencin g, has revolutionized our understanding of microbial communities.” Our news editors obtained a quote from the research from Northwest University, “ Here, we aimed to use a novel multiplex PCR test to evaluate the microbial compo sition and dominant lactobacilli in nonpregnant women with BV, and combined wit h machine learning algorithms to determine its diagnostic significance. Residual material of 288 samples of vaginal secretions derived from the vagina from heal thy women and BV patients that were sent for routine diagnostics was collected a nd subjected to the mPCR test. Subsequently, Decision tree (DT), random forest ( RF), and support vector machine (SVM) hybrid diagnostic models were constructed and validated in a cohort of 99 women that included 74 BV patients and 25 health y controls, and a separate cohort of 189 women comprising 75 BV patients, 30 int ermediate vaginal microbiota subjects and 84 healthy controls, respectively. The rate or abundance of and were significantly reduced in BV-affected patients whe n compared with healthy women, while , , BVAB2, 2, and were significantly increa sed. Then the hybrid diagnostic models were constructed and validated by an inde pendent cohort. The model constructed with support vector machine algorithm achi eved excellent prediction performance (Area under curve: 0.969, sensitivity: 90. 4%, specificity: 96.1%). Moreover, for subjects with a Nugent score of 4 to 6, the SVM-BV model might be more robust and sensitive tha n the Nugent scoring method. The application of this mPCR test can be effectivel y used in key vaginal microbiota evaluation in women with BV, intermediate vagin al microbiota, and healthy women.”
Xi’anPeople’s Republic of ChinaAsiaArtificial IntelligenceBacterial Infections and MycosesBacterial VaginosisDiagnostics and ScreeningEmerging TechnologiesGynecologyHealth and Medici neMachine LearningMultiplex PCRSupport Vector MachinesVaginal Diseases a nd ConditionsVaginosisVector MachinesWomen’s Health
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