Multimodal Assessment of Corneal Erosions Using Optical Coherence Tomography and Automated Grading of Fluorescein Staining in a Rabbit Dry Eye Model

Sher, Ifat; Tzameret, Adi; Szalapak, Alicja M.; Carmeli, Tomer; Derazne, Estela; Avni-Zauberman, Noa; Marcovich, Arie; Simon, Guy Ben; Rotenstreich, Ygal

doi:10.1167/tvst.8.1.27

Cited by 12 publications

(12 citation statements)

References 22 publications

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“…In a recent study, it has been reported that a significant correlation exists between the percentage of corneal fluorescein staining area and the percentage of the area of epithelial erosion detected by AS-OCT, in rabbits with experimental dry eye models. 28 Abou Shousha et al reported that ultra-high resolution OCT reveals irregularity of epithelial surface, which can be improved after treatment in patients with dry eye. 20 Another study by Deinema et al investigated the corneal reflectivity using AS-OCT in patients with DED and healthy subjects.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Autonomous AS-OCT Deep Learning Algorithm and Clinical Dry Eye Tests in Diagnosis of Dry Eye Disease

Chase¹,

Elsawy²,

Eleiwa³

et al. 2021

OPTH

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Objective To evaluate a deep learning-based method to autonomously detect dry eye disease (DED) in anterior segment optical coherence tomography (AS-OCT) images compared to common clinical dry eye tests. Methods In this study, 27,180 AS-OCT images were prospectively collected from 151 eyes of 91 patients. Images were used to train and test the deep learning model. Masked cornea specialist ophthalmologist diagnoses were used as the gold standard. Clinical dry eye tests were performed on patients in the DED group to compare the results of the model. The dry eye tests performed were tear break-up time (TBUT), Schirmer's test, corneal staining, conjunctival staining, and Ocular Surface Disease Index (OSDI). Results Our deep learning model achieved an accuracy of 84.62%, sensitivity of 86.36%, and specificity of 82.35% in the diagnosis of DED. The positive likelihood ratio was 4.89, and the negative likelihood ratio was 0.17. The mean DED probability score was 0.81 ± 0.23 in the DED group and 0.20 ± 0.27 in the healthy group (P < 0.01). The deep learning model accuracy in the diagnosis of DED was significantly better than that of corneal staining, conjunctival staining, and Schirmer's test (P < 0.05). There was no significant difference between the deep learning diagnostic accuracy and that of the OSDI and TBUT. Conclusion Based on preliminary results, reliable autonomous diagnosis of DED with our deep learning model was achieved, when compared with standard dry eye clinical tests that correlated significantly more or similarly to diagnoses made by cornea specialist ophthalmologists.

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Section: Discussionmentioning

confidence: 99%

Comparison of Autonomous AS-OCT Deep Learning Algorithm and Clinical Dry Eye Tests in Diagnosis of Dry Eye Disease

Chase¹,

Elsawy²,

Eleiwa³

et al. 2021

OPTH

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“…Fluorescein staining was performed by instilling 20 μL 2% fluorescein solution on the ocular surface. 16 After removing excess fluorescein, the cornea and the superior bulbar conjunctiva were photographed with a digital camera under blue light. The extent of fluorescein staining of the cornea and superior conjunctiva was scored using a modified NEI scoring method.…”

Section: Methodsmentioning

confidence: 99%

Once-Daily Topical Phosphosulindac Is Efficacious in the Treatment of Dry Eye Disease: Studies in Rabbit Models of Its Main Clinical Subtypes

Huang

et al. 2022

Journal of Ocular Pharmacology and Therapeutics

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Purpose: Dry eye disease (DED) is classified as aqueous deficient, evaporative, or mixed. We investigated the therapeutic effect of the novel anti-inflammatory drug phosphosulindac (PS) in rabbit models of DED encompassing its pathogenesis, and its transition to chronicity. Methods: We treated three rabbit models of DED with PS (hydrogel formulation) or vehicle topically applied 1 × /day. We induced aqueous-deficient DED (acute and chronic) by injecting Concanavalin A into lacrimal glands; evaporative DED by injecting into the upper eyelid inactivated Mycobacterium tuberculosis in complete Freund's adjuvant; and mixed DED through desiccative stress, induced by holding open the eye for 3 h. We determined corneal sensitivity, tear break-up time (TBUT), Schirmer's tear test (STT), tear osmolality, and fluorescein staining of the ocular surface. Results: PS reversed all abnormal DED parameters. In acute DED, PS dose dependently normalized corneal sensitivity and tear osmolality; and improved TBUT, STT, and fluorescein staining. PS normalized corneal sensitivity and improved all other parameters in chronic aqueous-deficient DED. In evaporative DED, PS normalized corneal sensitivity and improved TBUT and fluorescein staining (osmolality and STT were not significantly changed in this model). In the desiccative stress model, PS improved TBUT and fluorescein staining but had no effect on STT or tear osmolality. Conclusions: PS rapidly reversed almost all DED parameters in its three subtypes. The normalization of the suppressed corneal sensitivity suggests the possibility of marked symptomatic relief by PS. The hydrogel formulation allows once-daily dosing. PS merits further development as a potential treatment for DED.

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“…The corneal damage can be severe, depending on the duration of the desiccative stress. The model has been used to screen drug effects or to study the contribution of desiccative stress to the pathophysiology of DED ( 63 , 94 ).…”

Section: Rabbit Models Of Dedmentioning

confidence: 99%

Animal models of dry eye disease: Useful, varied and evolving (Review)

et al. 2021

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Dry eye disease (DED), which is a prevalent disease that still lacks successful treatment options, remains a major challenge in ophthalmology. Multiple animal models of DED have been used to decipher its pathophysiology and to develop novel treatments. These models use mice, rats, rabbits, cats, dogs and non-human primates. Each model assesses aspects of DED by focusing on elements of the lacrimal functional unit, which controls the homeostasis of the tear film. The present review outlines representative DED animal models and assesses their contribution to the study of DED. Murine models are the most extensively used, followed by rabbit models; the latter offer the advantage of larger eyes, a favorable biochemical profile for drug studies, experimental ease and relatively low cost, contrasting with non-human primates, which, although closer to humans, are not as accessible and are expensive. No comprehensive ‘ideal’ animal model encompassing all aspects of human DED exists nor is it feasible. Investigators often choose an animal model based on their experimental needs and the following four features of a given model: The size of the eye, its biochemical composition, the available research reagents and cost. As research efforts in DED expand, more refined animal models are needed to supplement the enormous contribution made to date by existing models.

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Multimodal Assessment of Corneal Erosions Using Optical Coherence Tomography and Automated Grading of Fluorescein Staining in a Rabbit Dry Eye Model

Cited by 12 publications

References 22 publications

Comparison of Autonomous AS-OCT Deep Learning Algorithm and Clinical Dry Eye Tests in Diagnosis of Dry Eye Disease

Comparison of Autonomous AS-OCT Deep Learning Algorithm and Clinical Dry Eye Tests in Diagnosis of Dry Eye Disease

Once-Daily Topical Phosphosulindac Is Efficacious in the Treatment of Dry Eye Disease: Studies in Rabbit Models of Its Main Clinical Subtypes

Animal models of dry eye disease: Useful, varied and evolving (Review)

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