Burkelindholm6208

PURPOSE To assess the diagnostic performance and generalizability of logistic regression in classifying primary vitreoretinal lymphoma (PVRL) versus uveitis from intraocular cytokine levels in a single-center retrospective cohort, comparing a logistic regression model and previously published Interleukin Score for Intraocular Lymphoma Diagnosis (ISOLD) scores against the interleukin 10 (IL-10)-to-interleukin 6 (IL-6) ratio. DESIGN Retrospective cohort study. PARTICIPANTS Patient histories, pathology reports, and intraocular cytokine levels from 2339 patient entries in the National Eye Institute Histopathology Core database. METHODS Patient diagnoses of PVRL versus uveitis and associated aqueous or vitreous IL-6 and IL-10 levels were collected retrospectively. From these data, cytokine levels were compared between diagnoses with the Mann-Whitney U test. A logistic regression model was trained to classify PVRL versus uveitis from aqueous and vitreous IL-6 and IL-10 samples and compared with ISOLD scores and IL-r the IL-10-to-IL-6 ratio. All models achieved complete separation between uveitis and lymphoma in the aqueous data set. CONCLUSIONS The accuracy of the logistic regression model and generalizability of the ISOLD score to an independent patient cohort suggest that intraocular cytokine analysis by logistic regression may be a promising adjunct to cytopathologic analysis, the gold standard, for the early diagnosis of primary vitreoretinal lymphoma. Further validation studies are merited. Published by Elsevier Inc.PURPOSE Aggressive posterior retinopathy of prematurity (AP-ROP) is a vision-threatening disease with a significant rate of progression to retinal detachment. The purpose of this study was to characterize AP-ROP quantitatively by demographics, rate of disease progression, and a deep learning-based vascular severity score. DESIGN Retrospective analysis. PARTICIPANTS The Imaging and Informatics in ROP cohort from 8 North American centers, consisting of 947 patients and 5945 clinical eye examinations with fundus images, was used. Pretreatment eyes were categorized by disease severity none, mild, type 2 or pre-plus, treatment-requiring (TR) without AP-ROP, TR with AP-ROP. Analyses compared TR with AP-ROP and TR without AP-ROP to investigate differences between AP-ROP and other TR disease. METHODS A reference standard diagnosis was generated for each eye examination using previously published methods combining 3 independent image-based gradings and 1 ophthalmoscopic grading. All fundus images were analyzed using aes with clinically identified categories of disease, including AP-ROP. The rate of progression to peak disease is greatest in eyes that demonstrate AP-ROP compared with other treatment-requiring eyes. Analysis of quantitative characteristics of AP-ROP may help improve diagnosis and treatment of an aggressive, vision-threatening form of ROP. PURPOSE To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA. DESIGN Prospective, multicenter, natural history study with up to 15 years of follow-up. PARTICIPANTS Four hundred nine CFIs of 238 eyes with GA from the Rotterdam Study (RS) and Blue Mountain Eye Study (BMES) for model development, and 3589 CFIs of 376 eyes from the Age-Related Eye Disease Study (AREDS) for analysis of GA growth rate. METHODS A deep learning model based on an ensemble of encoder-decoder architectures was implemented and optimized for the segmentation of GA in CFIs. Four experienced graders delineated, in consensus, GA in CFIs from the RS and BMES. Ac-DEVD-CHO research buy These manual delineations were used to evaluate the segmentation model using 5-fold cross-validation. The model was applied further to CFIs from the AREDS to study the growth rate of GA. Linear regression analysis was used to study associations between st and robust segmentation of GA on CFIs. These segmentations can be used to extract structural characteristics of GA that predict its growth rate. PURPOSE To investigate the change in the prevalence of reduced visual acuity (VA) in Taiwanese school children after a policy intervention promoting increased time outdoors. DESIGN Prospective cohort study based on the Taiwan School Student Visual Acuity Screen (TSVAS) by the Ministry of Education in Taiwan. PARTICIPANTS All school children from grades 1 through 6 were enrolled in the TSVAS from 2001 through 2015. METHODS The TSVAS requires each school in Taiwan to perform measurements of uncorrected VA (UCVA) on all students in grades 1 through 6 every half year using a Tumbling E chart. Reduced VA was defined as UCVA of 20/25 or less. Data from 1.2 to 1.9 million primary school children each year were collected from 2001 through 2015. A policy program named Tian-Tian 120 encouraged schools to take students outdoors for 120 minutes every day for myopia prevention. It was instituted in September 2010. To investigate the impact of the intervention, a segmented regression analysis of interrupted time series wasn Taiwan. Because randomized trials have demonstrated outdoor exposure slowing myopia onset, interventions to promote increased time outdoors may be useful in other areas affected by an epidemic of myopia. PURPOSE To examine the normative profile and determinants of macular ganglion cell-inner plexiform layer (GCIPL) thickness based on spectral-domain OCT (SD-OCT) in a nonglaucoma, multi-ethnic Asian population. DESIGN Population-based, cross-sectional study. PARTICIPANTS Ethnic Chinese, Malay, and Indian adults aged ≥40 years recruited from the Singapore Epidemiology of Eye Diseases Study. METHODS All participants underwent standardized examinations. The GCIPL thickness was measured using Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA). Participants with glaucoma or poor-quality scans were excluded. Eye-specific data were used. Associations of ocular and systemic factors with GCIPL thickness parameters were investigated using multivariable linear regression with generalized estimating equation models to account for correlation between both eyes. MAIN OUTCOME MEASURES GCIPL thickness. RESULTS A total of 4464 participants (7520 eyes) consisting of 1625 Chinese, 1212 Malay, and 1627 Indian adults contributed to this analysis.