Central Serous Chorioretinopathy Analyzed by Multimodal Imaging

Han, Liang; Carvalho, José Ronaldo Lima de; Parmann, Rait; Tezel, Tongalp H.; Chang, Stanley; Sharma, Tarun; Sparrow, Janet R.

doi:10.1167/tvst.10.1.15

Cited by 9 publications

(11 citation statements)

References 29 publications

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“…Ophthalmologists should be involved in the examination and follow-up of patients in cooperation with trained specialists of other professional profiles, usually rheumatologists from the beginning of the diagnosis of SLE, even in the case where the disease remains clinically silent and patients do not show ophthalmic symptoms. Physicians undertaking the examination of patients with autoimmune diseases should perform basic ophthalmic examinations: distant and near visual acuity, color perception, visual field examination, intraocular pressure measurement, Amsler grid, thorough examination of the anterior segment of the eye with a slit lamp, indirect and direct ophthalmoscopy of the posterior segment of the eye, as well as additional specialist examinations: mfERG, flash-VEP, BM, OCT, FAF, FFA, and ICGA [ 5 , 6 ].…”

Section: Ophthalmic Manifestations Of Slementioning

confidence: 99%

Understanding Ocular Findings and Manifestations of Systemic Lupus Erythematosus: Update Review of the Literature

Luboń

Kotyla

et al. 2022

IJMS

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Systemic lupus erythematosus (SLE) is a chronic multisystem autoimmune disease. Up to one-third of patients suffering from SLE have various ocular manifestations. The ocular findings may represent the initial manifestation of the systemic disease and may lead to severe ocular complications, and even loss of vision. Ocular manifestations are often associated with degree of systemic inflammation, but also can precede the occurrence of systemic symptoms. Early diagnosis and adequate management of patients with SLE are crucial and require cooperation between various specialists. Proper preparation of ophthalmologists can help to differentiate between complication of SLE and other ocular disorders. New therapies for SLE are promising for potential benefits, however, ocular side effects are still unknown.

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Section: Ophthalmic Manifestations Of Slementioning

confidence: 99%

Understanding Ocular Findings and Manifestations of Systemic Lupus Erythematosus: Update Review of the Literature

Luboń

Kotyla

et al. 2022

IJMS

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“…2 d). Dabei sind die Areale in der NIA größer als in der FAF, die NIA ist daher besonders geeignet, alle fovealen oder parafovealen Läsionen zu entdecken [12]. Granuläre Veränderungen in der NIA persistieren länger als in der FAF und korrelieren mit einer reduzierten retinalen Sensitivität [13].…”

Section: Zentrale Seröse Chorioretinopathie (Zscr)unclassified

Nahinfrarot-Autofluoreszenz: klinische Anwendung und diagnostische Relevanz

Kellner¹,

Weinitz²,

Farmand³

et al. 2022

Klin Monbl Augenheilkd

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ZusammenfassungDie Nahinfrarot-Autofluoreszenz (NIA) ist ein nicht-invasives Verfahren zur Untersuchung des retinalen Pigmentepithels (RPE) basierend auf der Darstellung des antioxidativen Schutzfaktors Melanin in den RPE-Zellen. Die NIA verbessert die Früherkennung chorioretinaler Erkrankungen, da bei vielen dieser Erkrankungen mit der NIA Strukturveränderungen des RPE nachweisbar sind, bevor sich in anderen Untersuchungen Krankheitszeichen erkennen lassen.

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“…Currently, the diagnostic workup for CSC relies on posterior segment optical coherence tomography (OCT). 5 The use of OCT enables ophthalmologists to effectively evaluate SRF by capturing cross-sectional views and whole three-dimensional volume of the macular-centered retina. However, OCT imaging requires bulky equipment and an experienced examiner, which is not appropriate in a disease screening setting.…”

Section: Introductionmentioning

confidence: 99%

Simple Code Implementation for Deep Learning–Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography

Yoo

Kim

Jeong

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose Central serous chorioretinopathy (CSC) is a retinal disease that frequently shows resolution and recurrence with serous detachment of the neurosensory retina. Here, we present a deep learning analysis of subretinal fluid (SRF) lesion segmentation in fundus photographs to evaluate CSC. Methods We collected 194 fundus photographs of SRF lesions from the patients with CSC. Three graders manually annotated of the entire SRF area in the retinal images. The dataset was randomly separated into training (90%) and validation (10%) datasets. We used the U-Net segmentation model based on conditional generative adversarial networks (pix2pix) to detect the SRF lesions. The algorithms were trained and validated using Google Colaboratory. Researchers did not need prior knowledge of coding skills or computing resources to implement this code. Results The validation results showed that the Jaccard index and Dice coefficient scores were 0.619 and 0.763, respectively. In most cases, the segmentation results overlapped with most of the reference areas in the annotated images. However, cases with exceptional SRFs were not accurate in terms of prediction. Using Colaboratory, the proposed segmentation task ran easily in a web-based environment without setup or personal computing resources. Conclusions The results suggest that the deep learning model based on U-Net from the pix2pix algorithm is suitable for the automatic segmentation of SRF lesions to evaluate CSC. Translational Relevance Our code implementation has the potential to facilitate ophthalmology research; in particular, deep learning–based segmentation can assist in the development of pathological lesion detection solutions.

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Central Serous Chorioretinopathy Analyzed by Multimodal Imaging

Cited by 9 publications

References 29 publications

Understanding Ocular Findings and Manifestations of Systemic Lupus Erythematosus: Update Review of the Literature

Understanding Ocular Findings and Manifestations of Systemic Lupus Erythematosus: Update Review of the Literature

Nahinfrarot-Autofluoreszenz: klinische Anwendung und diagnostische Relevanz

Simple Code Implementation for Deep Learning–Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography

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