Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids
ABSTRACT 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 flui...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2025-01-01
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| Series: | mSystems |
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| Online Access: | https://journals.asm.org/doi/10.1128/msystems.01058-24 |
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| author | Xin-Ru Wen Jia-Wei Tang Jie Chen Hui-Min Chen Muhammad Usman Quan Yuan Yu-Rong Tang Yu-Dong Zhang Hui-Jin Chen Liang Wang |
| author_facet | Xin-Ru Wen Jia-Wei Tang Jie Chen Hui-Min Chen Muhammad Usman Quan Yuan Yu-Rong Tang Yu-Dong Zhang Hui-Jin Chen Liang Wang |
| author_sort | Xin-Ru Wen |
| collection | DOAJ |
| description | ABSTRACT 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.IMPORTANCEThe 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. |
| format | Article |
| id | doaj-art-d81fa891ae5449f686b87d07499b8004 |
| institution | Kabale University |
| issn | 2379-5077 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mSystems |
| spelling | doaj-art-d81fa891ae5449f686b87d07499b80042025-01-21T14:00:28ZengAmerican Society for MicrobiologymSystems2379-50772025-01-0110110.1128/msystems.01058-24Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluidsXin-Ru Wen0Jia-Wei Tang1Jie Chen2Hui-Min Chen3Muhammad Usman4Quan Yuan5Yu-Rong Tang6Yu-Dong Zhang7Hui-Jin Chen8Liang Wang9School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaDepartment of Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, ChinaDepartment of Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, ChinaDepartment of Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, ChinaSchool of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaSchool of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaDepartment of Laboratory Medicine, Shengli Oilfield Central Hospital, Dongying, Shandong, ChinaSchool of 1st Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaDepartment of Laboratory Medicine, Shengli Oilfield Central Hospital, Dongying, Shandong, ChinaSchool of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaABSTRACT 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.IMPORTANCEThe 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.https://journals.asm.org/doi/10.1128/msystems.01058-24SERSmachine learningbacterial vaginosisdeep learningrapid identification |
| spellingShingle | Xin-Ru Wen Jia-Wei Tang Jie Chen Hui-Min Chen Muhammad Usman Quan Yuan Yu-Rong Tang Yu-Dong Zhang Hui-Jin Chen Liang Wang Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids mSystems SERS machine learning bacterial vaginosis deep learning rapid identification |
| title | Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids |
| title_full | Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids |
| title_fullStr | Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids |
| title_full_unstemmed | Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids |
| title_short | Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids |
| title_sort | rapid diagnosis of bacterial vaginosis using machine learning assisted surface enhanced raman spectroscopy of human vaginal fluids |
| topic | SERS machine learning bacterial vaginosis deep learning rapid identification |
| url | https://journals.asm.org/doi/10.1128/msystems.01058-24 |
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