Abstract
Fungal keratitis (FK) is a serious and sight-threatening corneal infection with global reach. The need for prompt diagnosis is paramount, as a delay in initiation of treatment could lead to irreversible vision loss. Current “gold standard” diagnostic methods, namely corneal smear and culture, have limitations due to diagnostic insensitivity and their time-consuming nature. PCR is a newer, complementary method used in the diagnosis of fungal keratitis, whose results are also sample-dependent. In vivo confocal microscopy (IVCM) is a promising complementary diagnostic method of increasing importance as it allows non-invasive real-time direct visualization of potential fungal pathogens and manifesting infection directly in the patient's cornea. In numerous articles and case reports, FK diagnosis by IVCM has been evaluated, and different features, approaches, sensitivity/specificity, and limitations have been noted. Here, we provide an up-to-date, comprehensive review of the current literature and present the authors' combined recommendations for fungal identification in IVCM images, while also looking to the future of FK assessment by IVCM using artificial intelligence methods.
Original language | English (US) |
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Pages (from-to) | 103-118 |
Number of pages | 16 |
Journal | Ocular Surface |
Volume | 24 |
DOIs | |
State | Published - Apr 2022 |
Keywords
- Artificial intelligence
- Cornea
- Diagnostics
- Fungal keratitis
- In vivo confocal microscopy
ASJC Scopus subject areas
- Ophthalmology