期刊
SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-02138-w
关键词
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资金
- Japanese Ministry of Education, Science, and Culture [17K11481, 21K09720, 21K09742]
- Grants-in-Aid for Scientific Research [21K09720, 21K09742, 17K11481] Funding Source: KAKEN
A deep learning-based algorithm was developed for computer-assisted diagnosis of infectious keratitis, showing high accuracy in distinguishing different pathogens and robustness even with low-resolution images.
Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the face of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse features and mitigate the uncertainty, batches of probability scores of 4 serial images taken from many angles or fluorescence staining were learned for score and decision level fusion using a gradient boosting decision tree. A total of 4306 slit-lamp images including 312 images obtained by internet publications on keratitis by bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV) were studied. The created algorithm had a high overall accuracy of diagnosis, e.g., the accuracy/area under the curve for acanthamoeba was 97.9%/0.995, bacteria was 90.7%/0.963, fungi was 95.0%/0.975, and HSV was 92.3%/0.946, by group K-fold validation, and it was robust to even the low resolution web images. We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.
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