From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
14 Dec 2023
14 Dec 2023
Historique:
received:
12
10
2023
accepted:
10
12
2023
medline:
15
12
2023
pubmed:
15
12
2023
entrez:
14
12
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38097753
doi: 10.1038/s41598-023-49635-8
pii: 10.1038/s41598-023-49635-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
22200Subventions
Organisme : NEI/NIH
ID : R01 EY024349 (ARD)
Organisme : Department of Defense
ID : Vision Research Program-Congressionally Directed Medical Research Program VR170180
Organisme : Research to Prevent Blindness
ID : Unrestricted Grant to the department and Physician-Scientist Award
Informations de copyright
© 2023. The Author(s).
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