Abstract
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.
Highlights
Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis
To obtain images of infectious keratitis, all of the 669 consecutive cases of suspected infectious keratitis that were referred to the Cornea Outpatient Clinic of the Tottori University Hospital between 2005 August and 2020 December, were assessed for the diagnosis based on the criteria
The top 4 categories of causative pathogens were bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV), and we focused on these 4 categories
Summary
Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. The major cause of the corneal opacities is infectious k eratitis[2], and slit-lamp examinations are the gold standard examination method to diagnose but to identify the causative pathogen in eyes with infectious keratitis. The purpose of this study was to develop hybrid deep learning (DL) algorithm that can determine the causative pathogen category in eyes with keratitis with a high probability score by analyzing slit-lamp images. We used facial recognition techniques[3] because the images of the faces are recorded from different angles, different levels of illuminations, and different degrees of resolution Using this approach, we determined the probability scores of the pathogen category that was causing the keratitis that can be used for machine learning classifications. The identification could avoid inappropriate treatments at the early stage of infection leading to an improvement of the visual outcomes
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