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From the Diagnosis of Infectious Keratitis to Discriminating Fungal Subtypes; a Deep Learning-Based Study Publisher Pubmed



Soleimani M1, 2 ; Esmaili K1 ; Rahdar A3 ; Aminizadeh M1 ; Cheraqpour K1 ; Tabatabaei SA1 ; Mirshahi R4 ; Bibak Z5 ; Mohammadi SF5 ; Koganti R2 ; Yousefi S6, 7 ; Djalilian AR2, 8
Authors
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Authors Affiliations
  1. 1. Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, United States
  3. 3. Department of Telecommunication, Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
  4. 4. Eye Research Center, The Five Senses Health Institute, Rasoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
  5. 5. Translational Ophthalmology Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
  6. 6. Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, United States
  7. 7. Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, United States
  8. 8. Cornea Service, Stem Cell Therapy and Corneal Tissue Engineering Laboratory, Illinois Eye and Ear Infirmary, 1855 W. Taylor Street, M/C 648, Chicago, 60612, IL, United States

Source: Scientific Reports Published:2023


Abstract

Infectious keratitis (IK) is a major cause of corneal opacity. IK can be caused by a variety of microorganisms. Typically, fungal ulcers carry the worst prognosis. Fungal cases can be subdivided into filamentous and yeasts, which shows fundamental differences. Delays in diagnosis or initiation of treatment increase the risk of ocular complications. Currently, the diagnosis of IK is mainly based on slit-lamp examination and corneal scrapings. Notably, these diagnostic methods have their drawbacks, including experience-dependency, tissue damage, and time consumption. Artificial intelligence (AI) is designed to mimic and enhance human decision-making. An increasing number of studies have utilized AI in the diagnosis of IK. In this paper, we propose to use AI to diagnose IK (model 1), differentiate between bacterial keratitis and fungal keratitis (model 2), and discriminate the filamentous type from the yeast type of fungal cases (model 3). Overall, 9329 slit-lamp photographs gathered from 977 patients were enrolled in the study. The models exhibited remarkable accuracy, with model 1 achieving 99.3%, model 2 at 84%, and model 3 reaching 77.5%. In conclusion, our study offers valuable support in the early identification of potential fungal and bacterial keratitis cases and helps enable timely management. © 2023, The Author(s).
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