Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images

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.

Infectious keratitis: A review

“…54 In areas or circumstances where patients are unable to access ophthalmic care, the ability to diagnose and assess microbial keratitis through artificial intelligence using external eye photos, such as could be taken with a mobile phone, may allow for appropriate therapy to be commenced without delay. 2,5,[54][55][56] 3 | HERPES SIMPLEX KERATITIS…”

“…Investigators using external eye photographs to assess deep learning frameworks in BK have reported that the diagnostic accuracy of different models ranged from 69% to 72%; comparable to ophthalmologists (66% to 74%) 54 . In areas or circumstances where patients are unable to access ophthalmic care, the ability to diagnose and assess microbial keratitis through artificial intelligence using external eye photos, such as could be taken with a mobile phone, may allow for appropriate therapy to be commenced without delay 2,5,54–56 …”

“…Deep learning algorithms are increasingly being recognised as having potential for screening and making management recommendations for patients with painful red eyes 54 ; distinguishing active corneal infection from scarring 55 ; and differentiating between causal organisms in keratitis 56 —for example between fungal and BK 57 . Convolutional neural networks apply very effectively deep learning for image classification.…”

Therapy for contact lens-related ulcers

“…Various reports have described the use of artificial intelligence deep learning to identify the presence of infectious keratitis, or determine type of infection based on imaging [45][46][47][48][49][50][51].…”

Diagnostic Performance of Deep Learning in Infectious Keratitis: A Systematic Review and Meta-Analysis Protocol

“…[22][23][24][25][26][27][28] Within the realm of ophthalmology, DL research previously focussed mainly on various posterior segment diseases (e.g., age-related macular degeneration, diabetic retinopathy, and glaucoma) and demonstrated comparable, if not better, diagnostic accuracy compared to healthcare professionals. 22,23,[29][30][31] Although several recent studies have demonstrated the potential of DL in assisting the diagnosis of IK and distinguishing IK from other ocular diseases, [32][33][34][35] the diagnostic accuracy of these DL models remains to be elucidated.…”