AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks
ABSTRACT Vulvovaginal candidiasis (VVC) is a prevalent fungal ailment affecting women globally. Timely and accurate diagnosis is crucial. Traditional methods, relying on clinical evaluation and manual microscopic examination, have limitations. Artificial intelligence (AI) offers potential improvemen...
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2025-01-01
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| Series: | Microbiology Spectrum |
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| Online Access: | https://journals.asm.org/doi/10.1128/spectrum.01691-24 |
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| _version_ | 1841556080968073216 |
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| author | Zhongxiao Wang Ruliang Wang Haichun Guo Qiannan Zhao Huijun Ren Jumin Niu Ying Wang Wei Wu Bingbing Liang Xin Yi Xiaolei Zhang Shiqi Xu Xianling Dong Liqun Wang Qinping Liao |
| author_facet | Zhongxiao Wang Ruliang Wang Haichun Guo Qiannan Zhao Huijun Ren Jumin Niu Ying Wang Wei Wu Bingbing Liang Xin Yi Xiaolei Zhang Shiqi Xu Xianling Dong Liqun Wang Qinping Liao |
| author_sort | Zhongxiao Wang |
| collection | DOAJ |
| description | ABSTRACT Vulvovaginal candidiasis (VVC) is a prevalent fungal ailment affecting women globally. Timely and accurate diagnosis is crucial. Traditional methods, relying on clinical evaluation and manual microscopic examination, have limitations. Artificial intelligence (AI) offers potential improvements in diagnostic accuracy and efficiency by objectively analyzing microscopic images of lower genital tract infections in women. A cascaded model was developed using 100,387 microscope images and 1,761 slides to diagnose VVC at slide level. The model’s diagnostic accuracy was compared with experts’. Five hundred thirteen slides were used to evaluate whether the experts’ diagnostic skills could be improved using the model as an AI-assisted tool. The consistency between experts’ interpretations of microscopic digital images and microscopic examination under eyepiece was assessed to determine whether the collected images adequately represented the slides. The model obtained AUC = 0.9447, 0.9711, and 0.9793 for slide-level diagnosing yeast hyphae, budding yeast, and yeast. Compared with the average performance of experts, the Youden indexes of our model’s best points were improved by 0.0069, 0.0772, 0.0579, and 0.0907 for yeast hyphae, budding yeast, yeast, and VVC. The average accuracy of the experts was improved by 5.98%, 5.20%, 4.82%, and 8.19% using our model as an AI-assisted tool. The consistency rates and Cohen’s kappa coefficients between experts’ interpretations of microscopic digital images and microscopic examination under eyepiece exceeded 93% and 0.83 for the three different morphologic states of yeast. Our model exhibits superior diagnostic accuracy for VVC compared to experts. Experts can significantly improve their own diagnostic accuracies by using our model as an AI-assisted tool. The microscope images collected from each slide effectively represent the slide.IMPORTANCEA cascaded deep neural network model was developed for slide-level diagnosis of vulvovaginal candidiasis (VVC), demonstrating superior diagnostic accuracy compared to experts. Experts significantly enhanced their diagnostic accuracies by utilizing our model as an AI-assisted tool. Therefore, this model holds potential for clinical application to aid in the diagnosis of VVC. |
| format | Article |
| id | doaj-art-40d8e7ad1ff14c8aa5cf1ce9bbed7a32 |
| institution | Kabale University |
| issn | 2165-0497 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | Microbiology Spectrum |
| spelling | doaj-art-40d8e7ad1ff14c8aa5cf1ce9bbed7a322025-01-07T14:05:18ZengAmerican Society for MicrobiologyMicrobiology Spectrum2165-04972025-01-0113110.1128/spectrum.01691-24AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networksZhongxiao Wang0Ruliang Wang1Haichun Guo2Qiannan Zhao3Huijun Ren4Jumin Niu5Ying Wang6Wei Wu7Bingbing Liang8Xin Yi9Xiaolei Zhang10Shiqi Xu11Xianling Dong12Liqun Wang13Qinping Liao14Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, ChinaDepartment of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, ChinaChangsha Hospital for Maternal & Child Health Care, Changsha, ChinaDepartment of Clinical Laboratory, Yantaishan Hospital, Yantai City, ChinaDepartment of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaShenyang Women’s and Children’s Hospital, Shenyang, ChinaDepartment of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, ChinaDepartment of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, ChinaBeijing Turing Medlab Co., Ltd., Beijing, ChinaBeijing Turing Medlab Co., Ltd., Beijing, ChinaHebei International Research Center of Medical Engineering, Chengde Medical University, Chengde, ChinaHebei International Research Center of Medical Engineering, Chengde Medical University, Chengde, ChinaHebei International Research Center of Medical Engineering, Chengde Medical University, Chengde, ChinaJiangxi Maternal & Child Health Hospital, Nanchang, ChinaDepartment of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, ChinaABSTRACT Vulvovaginal candidiasis (VVC) is a prevalent fungal ailment affecting women globally. Timely and accurate diagnosis is crucial. Traditional methods, relying on clinical evaluation and manual microscopic examination, have limitations. Artificial intelligence (AI) offers potential improvements in diagnostic accuracy and efficiency by objectively analyzing microscopic images of lower genital tract infections in women. A cascaded model was developed using 100,387 microscope images and 1,761 slides to diagnose VVC at slide level. The model’s diagnostic accuracy was compared with experts’. Five hundred thirteen slides were used to evaluate whether the experts’ diagnostic skills could be improved using the model as an AI-assisted tool. The consistency between experts’ interpretations of microscopic digital images and microscopic examination under eyepiece was assessed to determine whether the collected images adequately represented the slides. The model obtained AUC = 0.9447, 0.9711, and 0.9793 for slide-level diagnosing yeast hyphae, budding yeast, and yeast. Compared with the average performance of experts, the Youden indexes of our model’s best points were improved by 0.0069, 0.0772, 0.0579, and 0.0907 for yeast hyphae, budding yeast, yeast, and VVC. The average accuracy of the experts was improved by 5.98%, 5.20%, 4.82%, and 8.19% using our model as an AI-assisted tool. The consistency rates and Cohen’s kappa coefficients between experts’ interpretations of microscopic digital images and microscopic examination under eyepiece exceeded 93% and 0.83 for the three different morphologic states of yeast. Our model exhibits superior diagnostic accuracy for VVC compared to experts. Experts can significantly improve their own diagnostic accuracies by using our model as an AI-assisted tool. The microscope images collected from each slide effectively represent the slide.IMPORTANCEA cascaded deep neural network model was developed for slide-level diagnosis of vulvovaginal candidiasis (VVC), demonstrating superior diagnostic accuracy compared to experts. Experts significantly enhanced their diagnostic accuracies by utilizing our model as an AI-assisted tool. Therefore, this model holds potential for clinical application to aid in the diagnosis of VVC.https://journals.asm.org/doi/10.1128/spectrum.01691-24Vulvovaginal candidiasiscascaded neural networkslide levelmicroscopic images |
| spellingShingle | Zhongxiao Wang Ruliang Wang Haichun Guo Qiannan Zhao Huijun Ren Jumin Niu Ying Wang Wei Wu Bingbing Liang Xin Yi Xiaolei Zhang Shiqi Xu Xianling Dong Liqun Wang Qinping Liao AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks Microbiology Spectrum Vulvovaginal candidiasis cascaded neural network slide level microscopic images |
| title | AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks |
| title_full | AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks |
| title_fullStr | AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks |
| title_full_unstemmed | AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks |
| title_short | AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks |
| title_sort | ai assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks |
| topic | Vulvovaginal candidiasis cascaded neural network slide level microscopic images |
| url | https://journals.asm.org/doi/10.1128/spectrum.01691-24 |
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