Wen Xin-Ru, Tang Jia-Wei, Chen Jie, Chen Hui-Min, Usman Muhammad, Yuan Quan, Tang Yu-Rong, Zhang Yu-Dong, Chen Hui-Jin, Wang Liang
School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Department of Laboratory Medicine, Shengli Oilfield Central Hospital, Dongying, Shandong, China.
mSystems. 2025 Jan 21;10(1):e0105824. doi: 10.1128/msystems.01058-24. Epub 2024 Dec 10.
Bacterial vaginosis (BV) is an abnormal gynecological condition caused by the overgrowth of specific bacteria in the vagina. This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS) with machine learning (ML) algorithms. Vaginal fluid samples were classified as BV positive or BV negative using the BVBlue Test and clinical microscopy, followed by SERS spectral acquisition to construct the data set. Preliminary SERS spectral analysis revealed notable disparities in characteristic peak features. Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. Gradient-weighted class activation mapping (Grad-CAM) was used to highlight important regions in the images for prediction. Moreover, the CNN model was blindly tested on SERS spectra of vaginal fluid samples collected from 40 participants with unknown BV infection status, achieving a prediction accuracy of 90.75% compared with the results of the BVBlue Test combined with clinical microscopy. This novel technique is simple, cheap, and rapid in accurately diagnosing bacterial vaginosis, potentially complementing current diagnostic methods in clinical laboratories.
The accurate and rapid diagnosis of bacterial vaginosis (BV) is crucial due to its high prevalence and association with serious health complications, including increased risk of sexually transmitted infections and adverse pregnancy outcomes. Although widely used, traditional diagnostic methods have significant limitations in subjectivity, complexity, and cost. The development of a novel diagnostic approach that integrates SERS with ML offers a promising solution. The CNN model's high prediction accuracy, cost-effectiveness, and extraordinary rapidity underscore its significant potential to enhance the diagnosis of BV in clinical settings. This method not only addresses the limitations of current diagnostic tools but also provides a more accessible and reliable option for healthcare providers, ultimately enhancing patient care and health outcomes.
细菌性阴道病（BV）是一种由阴道内特定细菌过度生长引起的异常妇科疾病。本研究旨在开发一种将表面增强拉曼散射（SERS）与机器学习（ML）算法相结合的新型BV检测方法。使用BVBlue检测和临床显微镜检查将阴道液样本分类为BV阳性或BV阴性，随后进行SERS光谱采集以构建数据集。初步的SERS光谱分析揭示了特征峰特征的显著差异。构建并优化了多个ML模型，其中卷积神经网络（CNN）模型达到了最高预测准确率99%。使用梯度加权类激活映射（Grad-CAM）突出显示图像中用于预测的重要区域。此外，CNN模型对从40名BV感染状况未知的参与者收集的阴道液样本的SERS光谱进行了盲测，与BVBlue检测结合临床显微镜检查的结果相比，预测准确率达到了90.75%。这种新技术在准确诊断细菌性阴道病方面简单、廉价且快速，有可能补充临床实验室目前的诊断方法。
由于细菌性阴道病（BV）的高患病率以及与严重健康并发症的关联，包括性传播感染风险增加和不良妊娠结局，其准确快速诊断至关重要。尽管传统诊断方法被广泛使用，但在主观性、复杂性和成本方面存在重大局限性。将SERS与ML相结合的新型诊断方法的开发提供了一个有前景的解决方案。CNN模型的高预测准确率、成本效益和非凡的快速性突出了其在临床环境中增强BV诊断的巨大潜力。这种方法不仅解决了当前诊断工具的局限性，还为医疗保健提供者提供了一种更易获得且可靠的选择，最终改善患者护理和健康结果。
