Search

During the 2020-21 academic year, COVID-19-related educational disruptions impacted school-based vision screenings. However, limited information regarding changes in vision screening and the number of students impacted has been reported. Delayed screenings can negatively impact students' referral to eye care providers, which may affect a child's ability to see clearly and academic success. This study aims to describe changes in school-based vision screening practices through a survey of National Association of School Nurses state representatives (n = 49). Among states with vision screenings mandates, participants reported that 23.7% (9/38) states waived screenings, 31.6% (12/38) continued screenings, and 36.8% (14/38) modified requirements, such as grades screened or assessments included (e.g., color vision and stereoacuity screenings). These results suggest that millions of students across the United States missed vision screenings during the 2020-21 academic year. Efforts by education and school health stakeholders should be directed towards addressing the pandemic-related disruption in vision screening.

Purpose To classify fleck lesions and assess artificial intelligence (AI) in identifying flecks in Stargardt disease (STGD). Methods A retrospective study of 170 eyes from 85 consecutive patients with confirmed STGD. Fundus autofluorescence images were extracted, and flecks were manually outlined. A deep learning model was trained, and a hold-out testing subset was used to compare with manually identified flecks and for graders to assess. Flecks were clustered using K-means clustering. Results Of the 85 subjects, 45 were female, and the median age was 37 years (IQR 25-59). A subset of subjects (n=41) had clearly identifiable fleck lesions, and an AI was successfully trained to identify these lesions (average Dice score of 0.53, n=18). The AI segmentation had smaller (0.018 compared with 0.034 mm2, p<0.001) but more numerous flecks (75.5 per retina compared with 40.0, p<0.001), but the total size of flecks was not different. The AI model had higher sensitivity to detect flecks but resulted in more false positives. There were two clusters of flecks based on morphology: broadly, one cluster of small round flecks and another of large amorphous flecks. The per cent frequency of small round flecks negatively correlated with subject age (r=-0.31, p<0.005). Conclusions AI-based detection of flecks shows greater sensitivity than human graders but with a higher false-positive rate. With further optimisation to address current shortcomings, this approach could be used to prescreen subjects for clinical research. The feasibility and utility of quantifying fleck morphology in conjunction with AI-based segmentation as a biomarker of progression require further study. [ABSTRACT FROM AUTHOR]

Methods During March 2021, we searched each state Departments of Education and Health websites to systematically collect the following: (1) information about state-mandated VS during the 2020-2021 academic cycle amid the COVID-19 pandemic and (2) guidance about VS missed during the academic year 2019-2020 due to COVID-19-related school closures. Results As of March 2021, 16 states had publicly available information from their state Department of Health or Education about VS requirements for the 2020-2021 academic year. Vision screenings (VS) are an important public health tool to detect children at risk for vision problems.[1] Early identification and treatment result in better long-term visual and academic outcomes.[2] In the United States, 39 states and the District of Columbia require VS for school-age children.[3] Often, school nurses or other trained staff conduct VS within the school setting. [Extracted from the article]

Objective: To cluster the diverse phenotypic features of Stargardt disease (STGD) using unsupervised clustering of multimodal retinal structure and function data., Design: Retrospective cross-sectional study., Subjects: Eyes of subjects with STGD and fundus autofluorescence (FAF), OCT, electroretinography (ERG), and microperimetry (MP) data available within 1 year of the baseline evaluation., Methods: A total of 46 variables from FAF, OCT, ERG, and MP results were recorded for subjects with STGD as defined per published criteria. Factor analysis of mixed data identified the most informative variables. Unsupervised hierarchical clustering and silhouette analysis identified the optimal number of clusters to classify multimodal phenotypes., Main Outcome Measures: Phenotypic clusters of STGD subjects and the corresponding cluster features., Results: We included 52 subjects and 102 eyes with a mean visual acuity (VA) at the time of multimodal testing of 0.69 ± 0.494 logarithm of minimum angle of resolution (20/63 Snellen). We identified 4 clusters of eyes. Compared to the other clusters, cluster 1 (n = 16) included younger subjects, VA greater than that of clusters 2 and 3, normal or moderately low total macular volume (TMV), greater preservation of scotopic and photopic ERG responses and fixation stability, less atrophy, and fewer flecks. Cluster 2 (n = 49) differed from cluster 1 mainly with less atrophy and relatively stable fixation. Cluster 3 (n = 10) included older subjects than clusters 1 and 2 and showed the lowest VA, TMV, ERG responses, and fixation stability, with extensive atrophy. Cluster 4 (n = 27) showed better VA, TMV similar to clusters 1 and 2, moderate ERG activity, stable fixation, and moderate-high atrophy and flecks., Conclusions: Reflecting the phenotypic complexity of STGD, an unsupervised clustering approach incorporating phenotypic measures can be used to categorize STGD eyes into distinct clusters. The clusters exhibit differences in structural and functional measures including quantity of flecks, extent of retinal atrophy, visual fixation accuracy, and ERG responses, among other features. If novel pharmacologic, gene, or cell therapy modalities become available in the future, the multimodal phenomap approach may be useful to individualize treatment decisions, and its utility in aiding prognostication requires further evaluation., Financial Disclosures: Proprietary or commercial disclosure may be found after the references., (© 2023 by the American Academy of Ophthalmology.)