Importance: Cohort studies could not only reveal associations between change of refractive components and onset/progression of myopia, but also risk factors, which is important for understanding mechanism and providing strategies.Background: Prevalence of myopia is high in Shanghai, being reported to be 52.2% in children aged 10 years old.Design: Cohort study.Participants: A total of 1856 students from six randomly selected primary schools in Baoshan District, Shanghai.Methods: Children underwent comprehensive ocular measurement, including axial length (AL), corneal curvature radius and cycloplegic auto‐refraction. Questionnaires about eye usage time were collected. Grade 1 students were followed for 4 years, and grade 2 and 3 students for 2 years.Main Outcome Measures: (i) Change of spherical equivalent (SE) and AL and (ii) risk factors for progression and incidence of myopia.Results: The average 2‐year progress of SE was 0.91D, 0.91D and 1.11D for grade 1, 2 and 3, respectively, and the average elongation of AL was 0.70 mm, 0.64 mm and 0.71 mm, respectively. Only parental myopia, but not near work time, near work diopter time, outdoor activity time or attending tutoring classes, was associated with myopia incidence and progression in the present population. Using baseline SE could be a simple and effective indicator for myopia prediction.Conclusions and Relevance: Incidence and progression of myopia is relatively high in schoolchildren in Shanghai compared with children of Western countries, East Asia and other parts of China. Effective strategies to control myopia prevalence are in urgent need.

Visual impairments and blindness are major public health concerns globally. Effective eye disease screening aided by artificial intelligence (AI) is a promising countermeasure, although it is challenged by practical constraints such as poor image quality in community screening. The recently developed ophthalmic foundation model RETFound has shown higher accuracy in retinal image recognition tasks. This study developed an RETFound-enhanced deep learning (DL) model for multiple-eye disease screening using real-world images from community screenings. Our results revealed that our DL model improved the sensitivity and specificity by over 15% compared with commercial models. Our model also shows better generalisation ability than AI models developed using traditional processes. Additionally, decision curve analysis underscores the higher net benefit of employing our model in both urban and rural settings in China. These findings indicate that the RETFound-enhanced DL model can achieve a higher net benefit in community-based screening, advocating its adoption in low- and middle-income countries to address global eye health challenges.

Objectives To report on: (a) overall myopia and high myopia prevalence, and (b) the impact of education on the spherical equivalent refractive error in children across Shanghai. Design Cross-sectional study. Setting Across all 17 districts of Shanghai. Participants 910 245 children aged 4–14 years from a school-based survey conducted between 2012 and 2013. Main outcome measures Data of children with non-cycloplegic autorefraction, visual acuity assessment and questionnaire were analysed (67%, n=6 06 476). Prevalence of myopia (≤−1.0 D) and high myopia (≤−5.0 D) was determined. We used a regression discontinuity design to determine the impact of school entry cut-off date (1 September) by comparing refractive errors at each age, for children born pre-September to post-1 September, and performed a multivariate analysis to explore risk factors associated with myopia. Data analysis was performed in 2017–2018. Results Prevalence rates of myopia and high myopia were 32.9% (95% CI: 32.8% to 33.1%) and 4.2% (95% CI: 4.1% to 4.2%), respectively. From 6 years of age onwards, children born pre-September were more myopic compared with those born post-1 September (ahead in school by 1 year, discontinuity at 6 years: −0.19 D (95% CI: −0.09 to −0.30 D); 14 years: −0.67 D (95% CI: −0.21 to −1.14 D)). Conclusions Our findings suggest that myopia is associated with education, that is primarily focused on near-based activities. Efforts to reduce the burden should be directed to public awareness, reform of education and health systems.

Background: Community-based telemedicine screening for diabetic retinopathy (DR) has been highly recommended worldwide. However, evidence from low- and middle-income countries (LMICs) on the choice between artificial intelligence (AI)–based and manual grading–based telemedicine screening is inadequate for policy making. Objective: The aim of this study was to test whether the AI model is more worthwhile than manual grading in community-based telemedicine screening for DR in the context of labor costs in urban China. Methods: We conducted cost-effectiveness and cost-utility analyses by using decision-analytic Markov models with 30 one-year cycles from a societal perspective to compare the cost, effectiveness, and utility of 2 scenarios in telemedicine screening for DR: manual grading and an AI model. Sensitivity analyses were performed. Real-world data were obtained mainly from the Shanghai Digital Eye Disease Screening Program. The main outcomes were the incremental cost-effectiveness ratio (ICER) and the incremental cost-utility ratio (ICUR). The ICUR thresholds were set as 1 and 3 times the local gross domestic product per capita. Results: The total expected costs for a 65-year-old resident were US $3182.50 and US $3265.40, while the total expected years without blindness were 9.80 years and 9.83 years, and the utilities were 6.748 quality-adjusted life years (QALYs) and 6.753 QALYs in the AI model and manual grading, respectively. The ICER for the AI-assisted model was US $2553.39 per year without blindness, and the ICUR was US $15,216.96 per QALY, which indicated that AI-assisted model was not cost-effective. The sensitivity analysis suggested that if there is an increase in compliance with referrals after the adoption of AI by 7.5%, an increase in on-site screening costs in manual grading by 50%, or a decrease in on-site screening costs in the AI model by 50%, then the AI model could be the dominant strategy. Conclusions: Our study may provide a reference for policy making in planning community-based telemedicine screening for DR in LMICs. Our findings indicate that unless the referral compliance of patients with suspected DR increases, the adoption of the AI model may not improve the value of telemedicine screening compared to that of manual grading in LMICs. The main reason is that in the context of the low labor costs in LMICs, the direct health care costs saved by replacing manual grading with AI are less, and the screening effectiveness (QALYs and years without blindness) decreases. Our study suggests that the magnitude of the value generated by this technology replacement depends primarily on 2 aspects. The first is the extent of direct health care costs reduced by AI, and the second is the change in health care service utilization caused by AI. Therefore, our research can also provide analytical ideas for other health care sectors in their decision to use AI.

Pixel pitch calibration is an essential step to make the fundus structures in the fundus image quantitatively measurable, which is important for the diagnosis and treatment of many diseases, e.g., diabetes, arteriosclerosis, hereditary optic atrophy, etc. The conventional calibration approaches require the specific parameters of the fundus camera or several specially shot images of the chess board, but these are generally not accessible, and the calibration results cannot be generalized to other cameras. Based on automated ROI (region of interest) and optic disc detection, the diameter ratio of ROI and optic disc (ROI–disc ratio) is quantitatively analyzed for a large number of fundus images. With the prior knowledge of the average diameter of an optic disc in fundus, the pixel pitch can be statistically estimated from a large number of fundus images captured by a specific camera without the availability of chess board images or detailed specifics of the fundus camera. Furthermore, for fundus cameras of FOV (fixed field-of-view), the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel. The average ROI–disc ratio is approximately constant, i.e., 6.404 ± 0.619 in the pixel, according to 40,600 fundus images, captured by different cameras, of 45° FOV. In consequence, the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel, and results show the pixel pitches of Canon CR2, Topcon NW400, Zeiss Visucam 200, and Newvision RetiCam 3100 cameras are 6.825 ± 0.666 μm, 6.625 ± 0.647 μm, 5.793 ± 0.565 μm, and 5.884 ± 0.574 μm, respectively. Compared with the manually measured pixel pitches, based on the method of ISO 10940:2009, i.e., 6.897 μm, 6.807 μm, 5.693 μm, and 6.050 μm, respectively, the bias of the proposed method is less than 5%. Since our method doesn’t require chess board images or detailed specifics, the fundus structures on the fundus image can be measured accurately, according to the pixel pitch obtained by this method, without knowing the type and parameters of the camera.

Purpose: Pre-myopia, a non-myopic refractive state, is a key concern for myopia prevention because of its association with a significantly higher risk of myopia in children under 3 years of age. Amid the myopia pandemic, its onset at younger ages is increasing, yet research on screening methods for myopia and pre-myopia in preschool children remains limited. This study aimed to establish effective noncycloplegic screening methods for myopia and pre-myopia in preschool children.Methods: This cross-sectional study included 16 kindergartens in Shanghai, China. Uncorrected distance visual acuity (UDVA) was recorded using a logMAR visual acuity chart. Pre- and post-cycloplegic refractions were obtained using an auto-refractor (TopconKR-800). Noncycloplegic axial length (AL) and corneal curvature radius (CR) were measured using the IOL Master-700. Logistic regression models were developed to establish accurate noncycloplegic screening methods for myopia and pre-myopia.Results: A total of 1,308 children with a mean age of 4.3 ± 0.9 years were included; among them 640 (48.9%) were girls. The myopia prevalence rate was 2.8% (n = 36), and the prevalence of pre-myopia was 21.9% (n = 286). Pre-myopia screening (cycloplegic spherical equivalent [SE] ≤ −0.5 < SE ≤0.75 diopters [D]) using UDVA exhibited an area under the receiver operating curve (AUC) of 0.52, noncycloplegic SE had an AUC of 0.70 and AL had an AUC of 0.63. The accuracy of combining the SE and AL/CR ratio was among the best with the least number of checks used, and the AUC was 0.74 for pre-myopia screening and 0.94 for myopia screening (cycloplegic SE ≤ −0.5 D). The addition of UDVA did not further improve the accuracy.Conclusion: Using UDVA alone did not achieve good accuracy in pre-myopia or myopia screening of young children. Under non-cycloplegic conditions, the combination of AL/CR and SE demonstrated favorable results for pre-myopia and myopia screening of preschool children.