Vaginal microbiota molecular profiling and diagnostic performance of artificial intelligence-assisted multiplex PCR testing in women with bacterial vaginosis: a single-center experience

TL;DR

This study uses a new PCR test and AI to analyze vaginal microbiota and diagnose bacterial vaginosis more accurately than traditional methods.

Contribution

A novel AI-assisted multiplex PCR test is introduced for diagnosing bacterial vaginosis with high accuracy.

Findings

Lactobacillus crispatus and Lactobacillus jensenii were reduced in BV patients, while other bacteria were increased.

The SVM-based model achieved 96.9% accuracy in predicting BV, outperforming traditional Nugent scoring for intermediate cases.

The mPCR test effectively evaluates vaginal microbiota in BV, intermediate, and healthy women.

Abstract

Bacterial vaginosis (BV) is a most common microbiological syndrome. The use of molecular methods, such as multiplex real-time PCR (mPCR) and next-generation sequencing, has revolutionized our understanding of microbial communities. Here, we aimed to use a novel multiplex PCR test to evaluate the microbial composition and dominant lactobacilli in non-pregnant women with BV, and combined with machine learning algorithms to determine its diagnostic significance.

Residual material of 288 samples of vaginal secretions derived from the vagina from healthy women and BV patients that were sent for routine diagnostics was collected and 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 healthy controls, and a separate cohort of 189 women comprising 75 BV patients, 30 intermediate vaginal microbiota subjects and 84 healthy controls, respectively.

The rate or abundance of Lactobacillus crispatus and Lactobacillus jensenii were significantly reduced in BV-affected patients when compared with healthy women, while Lactobacillus iners, Gardnerella vaginalis, Atopobium vaginae, BVAB2, Megasphaera type 2, Prevotella bivia, and Mycoplasma hominis were significantly increased. Then the hybrid diagnostic models were constructed and validated by an independent cohort. The model constructed with support vector machine algorithm achieved 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 than the Nugent scoring method.

The application of this mPCR test can be effectively used in key vaginal microbiota evaluation in women with BV, intermediate vaginal microbiota, and healthy women. In addition, this test may be used as an alternative to the clinical examination and Nugent scoring method in diagnosing BV.

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