2021
DOI: 10.1111/vop.12928
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Applications of in vivo confocal microscopy in the management of infectious keratitis in veterinary ophthalmology

Abstract: In vivo confocal microscopy (IVCM) is a relatively new ocular imaging technique that permits morphological and quantitative assessment of the living cornea on the cellular level. The applications for IVCM in clinical ophthalmology are numerous and diverse. There are several advantages inherent to IVCM over standard diagnostic techniques currently used to confirm a diagnosis of infectious keratitis in veterinary ophthalmology. With IVCM, images can be viewed in real-time providing immediate diagnostic informati… Show more

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Cited by 8 publications
(4 citation statements)
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References 87 publications
(243 reference statements)
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“…Corneal biopsy was unavailable, as they were client-owned dogs and showed improvement with ongoing treatment. Examination using in vivo confocal microscopy would have allowed visualization of the living cornea at the cellular level without invasive sampling procedures in the future [ 14 ].…”
Section: Discussionmentioning
confidence: 99%
“…Corneal biopsy was unavailable, as they were client-owned dogs and showed improvement with ongoing treatment. Examination using in vivo confocal microscopy would have allowed visualization of the living cornea at the cellular level without invasive sampling procedures in the future [ 14 ].…”
Section: Discussionmentioning
confidence: 99%
“…Standard diagnostic methods include sample collection, microscopic evaluation, and microbial culture following clinical presentations suggestive of fungal infection [ 1 2 3 ]. Advanced diagnostic imaging modalities, such as in vivo confocal microscopy and optical coherence tomography (OCT), provide clinicians with detailed structural features of the cornea in various pathological conditions non-invasively and in real time [ 5 6 ]. The usefulness of in vivo confocal microscopy for diagnosing fungal keratitis and monitoring treatment response has been reported in humans and dogs [ 2 4 ].…”
Section: Introductionmentioning
confidence: 99%
“…AI subclassifications include machine learning (ML), deep learning (DL), artificial neural networks (ANNs), deep neural networks (DNNs), convolution neural networks (CNNs), and transfer learning [7]. The utilization of AI can significantly benefit society and the healthcare system, given that the diagnosis of ocular conditions heavily relies on the experience of highly trained ophthalmologists whose distribution varies significantly across geographic areas [8,9]. Additionally, given the widespread use of imaging tools in clinical practice and the consequent availability of codified data from imaging to numeric clinical parameters, the ophthalmic community is positioned well to develop AI strategies [10].…”
Section: Introductionmentioning
confidence: 99%