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 p

SUBMITTER: Koyama A 

PROVIDER: S-EPMC8608802 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature