Wang Zhongxiao, Wang Ruliang, Guo Haichun, Zhao Qiannan, Ren Huijun, Niu Jumin, Wang Ying, Wu Wei, Liang Bingbing, Yi Xin, Zhang Xiaolei, Xu Shiqi, Dong Xianling, Wang Liqun, Liao Qinping
Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, China.
Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
Microbiol Spectr. 2025 Jan 7;13(1):e0169124. doi: 10.1128/spectrum.01691-24. Epub 2024 Nov 22.
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.
A 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.
外阴阴道念珠菌病（VVC）是一种在全球范围内影响女性的常见真菌性疾病。及时准确的诊断至关重要。传统方法依赖临床评估和手动显微镜检查，存在局限性。人工智能（AI）通过客观分析女性下生殖道感染的显微镜图像，有望提高诊断准确性和效率。利用100387张显微镜图像和1761张玻片开发了一种级联模型，用于在玻片水平诊断VVC。将该模型的诊断准确性与专家的进行比较。使用513张玻片评估作为人工智能辅助工具的该模型是否能提高专家的诊断技能。评估专家对显微数字图像的解读与目镜下显微镜检查之间的一致性，以确定所收集的图像是否能充分代表玻片。该模型在玻片水平诊断酵母菌丝、芽殖酵母和酵母时的AUC分别为0.9447、0.9711和0.9793。与专家的平均表现相比，我们模型最佳点的约登指数在酵母菌丝、芽殖酵母、酵母和VVC方面分别提高了0.0069、0.0772、0.0579和0.0907。使用我们的模型作为人工智能辅助工具，专家的平均准确率提高了5.98%、5.20%、4.82%和8.19%。对于酵母的三种不同形态状态，专家对显微数字图像的解读与目镜下显微镜检查之间的一致率和科恩kappa系数超过了93%和0.83。与专家相比，我们的模型对VVC表现出更高的诊断准确性。专家通过使用我们的模型作为人工智能辅助工具，可以显著提高自身的诊断准确性。从每张玻片收集的显微镜图像有效地代表了玻片。
开发了一种用于外阴阴道念珠菌病（VVC）玻片水平诊断的级联深度神经网络模型，与专家相比显示出更高的诊断准确性。专家通过将我们的模型用作人工智能辅助工具，显著提高了他们的诊断准确性。因此，该模型在临床应用中辅助诊断VVC具有潜力。
