Prevalence of Fundus Tessellation and Its Screening Based on Artificial Intelligence in Chinese Children: the Nanjing Eye Study.

INTRODUCTION: To investigate the prevalence of fundus tessellation (FT), and the threshold for screening FT using an artificial intelligence (AI) technology in Chinese children. METHODS: The Nanjing Eye Study was a population-based cohort study conducted in children born between September 2011 and August 2012 in Yuhuatai District of Nanjing. The data presented in this paper were obtained in 2019, when these children were 7 years old and underwent 45° non-mydriatic fundus photography. FT in whole fundus, macular area, and peripapillary area was manually recognized from fundus photographs and classified into three grades. Fundus tessellation density (FTD) in these areas was obtained by calculating the average exposed choroid area per unit area using artificial intelligence (AI) technology based on fundus photographs. The threshold for screening FT using FTD was determined using receiver operating characteristic (ROC) curve analysis. RESULTS: Among 1062 enrolled children (mean [± standard deviation] spherical equivalent: - 0.28 ± 0.70 D), the prevalence of FT was 42.18% in the whole fundus (grade 1: 36.53%; grade 2: 5.08%; grade 3: 0.56%), 45.57% in macular area (grade 1: 43.5%; grade 2: 1.60%; grade 3: 0.50%), and 49.72% in peripapillary area (grade 1: 44.44%; grade 2: 4.43%; grade 3: 0.85%), respectively. The threshold value of FTD for screening severe FT (grade ≥ 2) was 0.049 (area under curve [AUC] 0.985; sensitivity 98.3%; specificity 92.3%) in the whole fundus, 0.069 (AUC 0.987; sensitivity 95.5%; specificity 96.2%) in the macular area, and 0.094 (AUC 0.980; sensitivity 94.6%; specificity 94.2%) in the peripapillary area, respectively. CONCLUSION: Fundus tessellation affected approximately 40 in 100 children aged 7 years in China, indicating the importance and necessity of early FT screening. The threshold values of FTD provided by this study had high accuracy for detecting severe FT and might be applied for rapid screening.

Fundus Tessellated Density Assessed by Deep Learning in Primary School Children.

Purpose: To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning. Methods: Comprehensive ocular examinations were conducted in 577 children aged 7 years old from a population-based cross-sectional study, including biometric measurement, refraction, optical coherence tomography angiography, and 45° nonmydriatic fundus photography. FTD was defined as the average exposed choroid area per unit area of the fundus, and obtained by artificial intelligence technology. The distribution of FT was classified into the macular pattern and the peripapillary pattern according to FTD. Results: The mean FTD was 0.024 ± 0.026 in whole fundus. Multivariate regression analysis showed that greater FTD was significantly correlated with thinner subfoveal choroidal thickness, larger parapapillary atrophy, greater vessel density inside the optic disc, larger vertical diameter of optic disc, thinner retinal nerve fiber layer, and longer distance from optic disc center to macular fovea (all P < 0.05). The peripapillary distributed group had larger parapapillary atrophy (0.052 ± 0.119 vs 0.031 ± 0.072), greater FTD (0.029 ± 0.028 vs 0.015 ± 0.018), thinner subfoveal choroidal thickness (297.66 ± 60.61 vs 315.33 ± 66.46), and thinner retinal thickness (285.55 ± 10.89 vs 288.03 ± 10.31) than the macular distributed group (all P < 0.05). Conclusions: FTD can be applied as a quantitative biomarker to estimate subfoveal choroidal thickness in children. The role of blood flow inside optic disc in FT progression needs further investigation. The distribution of FT and the peripapillary pattern correlated more with myopia-related fundus changes than the macular pattern. Translational Relevance: Artificial intelligence can evaluate FT quantitatively in children, and has potential value for assisting in myopia prevention and control.

Instrument referral criteria for PlusoptiX and SureSight based on 2021 AAPOS guidelines: A population-based study.

Objective: The study aims to assess two refractive instrument-based methods of vision screening (SureSight and PlusoptiX) to detect refractive amblyopia risk factors (ARFs) and significant refractive errors in Chinese preschool children and to develop referral criteria according to the 2021 AAPOS guidelines. Methods: Eye examinations were conducted in children aged 61 to 72 months (n = 1,173) using a PlusoptiX photoscreener, SureSight autorefractor, and cycloplegic retinoscopy (CR). The Vision Screening Committee of AAPOS's preschool vision screening guidelines from 2021 were adopted for comparison. Paired t-test analysis and Bland-Altman plots were used to assess the differences and agreement between the PlusoptiX photoscreener, SureSight autorefractor, and CR. In addition, the validity of the cut-off values of the several ARFs measured with the SureSight and PlusoptiX was estimated using receiver operating characteristic (ROC) curves and compared to the age-based 2021 AAPOS examination failure levels. Results: A total of 1,173 children were tested with comprehensive eye examinations. When the referral numbers based on the 2013 (43/3.67%) and 2021 (42/3.58%) AAPOS guidelines were compared, significant differences between the values of astigmatism (72.09 vs. 52.38%) and anisometropia (11.63 vs. 38.10%) were found. The 95% limits of agreement (LOA) of the spherical value and the cylindrical value between PlusoptiX and CR were 95.08 and 96.29%. It was 93.87 and 98.10% between SureSight and CR. Considering refractive failure levels, the ROC curves obtained the optimal cut-off points. However, the PlusoptiX and the SureSight showed lower efficiency in hyperopia (Youden index, 0.60 vs. 0.83) and myopia (Youden index, 078 vs. 0.93), respectively. After adjusting the above cut-off points, the optimized NES (Nanjing Eye Study) referral criteria for myopia, hyperopia, astigmatism, and anisometropia were -0.75, 1.25, -1.0, and 0.5 with PlusoptiX and -1.25, 2.75, -1.5, and 0.75 with SureSight. Conclusions: SureSight and PlusoptiX showed a good correlation with CR and could effectively detect refractive ARFs and visually significant refractive errors. There were obvious advantages in detecting hyperopia using SureSight and myopia using PlusoptiX. We proposed instrumental referral criteria for age-based preschool children based on AAPOS 2021 guidelines.

Prevalence of Self-Reported Symptoms of Computer Vision Syndrome and Associated Risk Factors among School Students in China during the COVID-19 Pandemic.

PURPOSE: To determine the prevalence and risk factors of self-reported symptoms of Computer Vision Syndrome (CVS) in school students during COVID-19 pandemic. METHODS: In this cross-sectional study in Nanjing, China, students (grades 1-12) from six randomly selected schools self-administered the online survey questionnaire about hours on electronic devices, outdoor activities, frequency and intensity of 17 CVS symptoms during virtual learning period of COVID-19 pandemic. RESULTS: Among 2363 invited students, 2005 (84.8%) completed the survey. Mean (SD) age was 12 (3.0) years, 51% were male, 53% wore glasses, 56% did not know the 20-20-20 rule (take a 20 second break every 20 min of close work and gaze at objects at least 20 feet away) and 6.5% knew but never followed the 20-20-20 rule. Mean (SD) hours/day was 4.6 (3.4) for screen time and 1.2 (1.0) for outdoor activities. Total of 1543 (77.0%) students reported at least one CVS symptom (median = 4) with eye dryness and itching the most common (48%). In multivariable analysis, CVS score 4 or more was independently associated with older age (adjusted odds ratio (aOR) = 1.05 for per-year increase, p = 0.02), children with myopia not wearing glasses (aOR = 2.12, p = 0.0003), astigmatism (aOR = 1.37, p = 0.04), other ocular diseases (aOR = 1.59, p = 0.005), not complying 20-20-20 rule (aOR = 2.13, p = 0.04), more screen time (aOR = 1.53 for >6 vs. ≤2 hours, p = 0.0001) and less outdoor activities (aOR = 1.37 for ≤0.6 vs. ≥1.9 hours, p = 0.005). CONCLUSIONS: During COVID-19 pandemic, self-reported CVS symptoms occurred in more than three-quarters of students. Besides pre-existing ocular diseases, children with myopia not wearing glasses, excessive time on screens, non-compliance of the 20-20-20 rule and reduced outdoor activities were associated with higher risk of CVS.

Improving K-12 Teachers' Acceptance of Open Educational Resources by Open Educational Practices: A Mixed Methods Inquiry.

Teachers in K-12 settings increasingly demand instructional materials beyond textbooks to follow the upward momentum of personalized instruction. Especially during the outbreak of COVID-19, K-12 teachers are forced to quickly adapt to online teaching and thus have more difficulties of delivering personalized instruction in a relatively resource-restraint situation. Open educational resources (OER), allowing teachers to retain, reuse, revise, remix, and redistribute high-quality educational resources at no costs, can be a viable option for teachers. However, the acceptance of OER in K-12 education still remains low. Effective strategies are needed to reinforce teacher intention to adopt OER. This research showcased a two-phase sequential explanatory mixed method inquiry to investigate whether engaging K-12 teachers in open educational practices (OEP)-such as renewable assignments-increased their acceptance of OER. The quantitative phase, referring to the technology acceptance model (TAM), examined the change in factors influencing teachers' acceptance of OER. The qualitative phase was followed up to provide supplementary perspectives of the quantitative findings. By integrating complementary findings, this research found that OEP increased teachers' perceived ease of and self-efficacy towards using OER. Although teachers' intention of implementing OER is not significantly improved, qualitative findings offered additional insights into the benefits of OEP in promoting OER usage and the recommended directions for future effort. Practical implications on improving teachers' acceptance of OER in K-12 curriculum are discussed at the end.