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Morphologic Classification and Automatic Diagnosis of Bacterial Vaginosis by Deep Neural Networks

By Zhongxiao Wang, Lei Zhang, Min Zhao, Ying Wang, Huihui Bai, Yufeng Wang, Can Rui, Chong Fan, Jiao Li, Na Li, Xinhuan Liu, Zitao Wang, Yanyan Si, Andrea Feng, Mingxuan Li, Qiongqiong Zhang, Zhe Yang, Mengdi Wang, Wei Wu, Yang Cao, Lin Qi, Xin Zeng, Li Geng, Ruifang An, Ping Li, Zhaohui Liu, Qiao Qiao, Weipei Zhu, Weike Mo, Qinping Liao, Wei Xu

Posted 20 May 2020
bioRxiv DOI: 10.1101/2020.05.20.101055

Background Bacterial vaginosis (BV) was the most common condition for women’s health caused by the disruption of normal vaginal flora and an overgrowth of certain disease-causing bacteria, affecting 30-50% of women at some time in their lives. Gram stain followed by Nugent scoring (NS) based on bacterial morphotypes under the microscope was long considered golden standard for BV diagnosis. This conventional manual method was often considered labor intensive, time consuming, and variable results from person to person. Methods We developed four convolutional neural networks (CNN) models, and evaluated their ability to automatic identify vaginal bacteria and classify Nugent scores from microscope images. All the CNN models were first trained with 23280 microscopic images labeled with Nugent scores from top experts. A separate set of 5815 images were evaluated by the CNN models. The best CNN model was selected to generalize its application on an independent sets of 1082 images collecting from three teaching hospitals. Different hardwares were used to take images in hospitals. Results Our model could classify three Nugent Scores from images with high three classification accuracy of 89.3% (with 82.4% sensitivity and 96.6% specificity) on the 5815 test images, which was better diagnostic yield than the top-level technologists and obstetricians in China. The ability of generalization for our model was strong that it obtained 75.1%, which was 6.6% higher than the average of technologists. Conclusion The CNN model over performed human healthcare practitioners on accuracy, efficiency and stability for BV diagnosis using microscopic image-based Nugent scores. The deep learning model may offer translational application in automating diagnosis of bacterial vaginosis with proper supporting hardware. ### Competing Interest Statement Authors W. Mo, W. Wu and M. Li were employed by the company Suzhou Turing Microbial Technologies Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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