The design and application of an automated microscope developed based on deep learning for fungal detection in dermatology
The design and application of an automated microscope developed based on deep learning for fungal detection in dermatology
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DOI：
10.1111/myc.13209
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发表时间：
2020-12-07
期刊：
影响因子：
4.9
通讯作者：
Huang, Huaiqiu
中科院分区：
文献类型：
--
作者：
Gao, Wenchao;Li, Meirong;Huang, Huaiqiu
背景光学显微镜研究皮肤标本真菌感染耗时长，需要自动化。目的设计和探索皮肤标本真菌检测的自动化显微镜的应用。采集皮肤、指甲和头发样本。以人类检测结果为金标准，计算自动显微镜检测真菌的灵敏度和特异度。结果建立了自动显微镜，并训练了基于ResNet-50的图像处理模型。共采集292份样本，其中皮肤样本236份，指甲样本50份，头发样本6份。自动显微镜检测皮肤、指甲和头发中真菌的灵敏度分别为99.5%、95.2%和60%，特异度分别为91.4%、100%和100%。结论研制的自动显微镜对皮肤和指甲中真菌的检测与人类检查员一样熟练，但在头发样本中的性能有待提高。
Background Light microscopy to study the infection of fungi in skin specimens is time-consuming and requires automation.Objective We aimed to design and explore the application of an automated microscope for fungal detection in skin specimens.Methods An automated microscope was designed, and a deep learning model was selected. Skin, nail and hair samples were collected. The sensitivity and the specificity of the automated microscope for fungal detection were calculated by taking the results of human inspectors as the gold standard.Results An automated microscope was built, and an image processing model based on the ResNet-50 was trained. A total of 292 samples were collected including 236 skin samples, 50 nail samples and six hair samples. The sensitivities of the automated microscope for fungal detection in skin, nails and hair were 99.5%, 95.2% and 60%, respectively, and the specificities were 91.4%, 100% and 100%, respectively.Conclusion The automated microscope we developed is as skilful as human inspectors for fungal detection in skin and nail samples; however, its performance in hair samples needs to be improved.