A deep learning approach in diagnosing fungal keratitis based on corneal photographs

Ming Tse Kuo*, Benny Wei Yun Hsu, Yu Kai Yin, Po Chiung Fang, Hung Yin Lai, Alexander Chen, Meng Shan Yu, Vincent S. Tseng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Fungal keratitis (FK) is the most devastating and vision-threatening microbial keratitis, but clinical diagnosis a great challenge. This study aimed to develop and verify a deep learning (DL)-based corneal photograph model for diagnosing FK. Corneal photos of laboratory-confirmed microbial keratitis were consecutively collected from a single referral center. A DL framework with DenseNet architecture was used to automatically recognize FK from the photo. The diagnoses of FK via corneal photograph for comparing DL-based models were made in the Expert and NCS-Oph group through a majority decision of three non-corneal specialty ophthalmologist and three corneal specialists, respectively. The average percentage of sensitivity, specificity, positive predictive value, and negative predictive value was approximately 71, 68, 60, and 78. The sensitivity was higher than that of the NCS-Oph (52%, P <.01), whereas the specificity was lower than that of the NCS-Oph (83%, P <.01). The average accuracy of around 70% was comparable with that of the NCS-Oph. Therefore, the sensitive DL-based diagnostic model is a promising tool for improving first-line medical care at rural area in early identification of FK.

Original languageEnglish
Article number14424
JournalScientific reports
Issue number1
StatePublished - 1 Dec 2020

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