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

Journal: Scientific reports
Published Date: Sep 2, 2020

Abstract

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.

Authors

  • Ming-Tse Kuo
    Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung, 833, Taiwan, ROC. mingtse@cgmh.org.tw.
  • Benny Wei-Yun Hsu
    Department of Computer Science, National Chiao Tung University, No. 1001, Daxue Rd., East Dist., Hsinchu, 300, Taiwan, ROC.
  • Yu-Kai Yin
    Department of Computer Science, National Chiao Tung University, No. 1001, Daxue Rd., East Dist., Hsinchu, 300, Taiwan, ROC.
  • Po-Chiung Fang
    Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung, 833, Taiwan, ROC.
  • Hung-Yin Lai
    Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung, 833, Taiwan, ROC.
  • Alexander Chen
    Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung, 833, Taiwan, ROC.
  • Meng-Shan Yu
    Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung, 833, Taiwan, ROC.
  • Vincent S Tseng
    Computer Science and Information Engineering, National Chiao Tung University, Hsinchu, Taiwan.

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