The Guangzhou Twin Eye Study (GTES) is a cohort of twins living in South China that has been longitudinally followed for more than 15 years. This study has extensively investigated the heritability of myopia and the influence of environmental factors, producing significant and far-reaching impacts. GTES has found a high heritability of axial length and peripheral refraction, the significant role of education in myopia progression, and established prediction model for myopia onset and progression. The study has also explore the impact of both genetic and environmental factors on myopia development. By reviewing the major findings on myopia from the GTES, we hope to better inform public health strategies and clinical practices aimed at mitigating the global myopia epidemic.

The Guangzhou Twin Eye Study (GTES) is a cohort of twins living in South China that has been longitudinally followed for more than 15 years. This study has extensively investigated the heritability of myopia and the influence of environmental factors, producing significant and far-reaching impacts. GTES has found a high heritability of axial length and peripheral refraction, the significant role of education in myopia progression, and established prediction model for myopia onset and progression. The study has also explore the impact of both genetic and environmental factors on myopia development. By reviewing the major findings on myopia from the GTES, we hope to better inform public health strategies and clinical practices aimed at mitigating the global myopia epidemic.

Background: Diabetic retinopathy (DR) is a top leading cause of blindness worldwide, requiring early detection for timely intervention. Artificial Intelligence (AI) has emerged as a promising tool to improve DR screening efficiency, accessibility, and cost-effectiveness. This study conducted a systematic review of literature and meta-analysis on the economic outcomes of AI-based DR screening. Methods: A systematic review of studies published before September 2024 was conducted throughout PubMed, Scopus, Embase, the Cochrane Library, the National Health Service Economic Evaluation Database, and the Cost-Effectiveness Analysis Registry. Eligible studies were included if they were (1) conducted among type 1 diabetes mellitus or type 2 diabetes mellitus  adult diabetic population; (2) studies compared AI-based DR screening strategy to non-AI screening; and (3) performed a cost-effectiveness analysis. Meta-analysis was applied to pool incremental net benefit (INB) across studies stratified by country income and study perspective using a random-effects model. Statistical heterogeneity among studies was assessed using the I² statistic, Cochrane Q statistics, and meta regression. Results: Nine studies were included in the analysis. From a healthcare system/payer perspective, AI-based DR screening was significantly cost-effective compared to non-AI-based screening, with a pooled INB of 615.77 (95% confidence interval [CI]: 558.27, 673.27). Subgroup analysis showed robust cost-effectiveness of AI-based DR screening in high-income countries (INB = 613.62, 95% CI: 556.06, 671.18) and upper-/lower- middle income countries (INB = 1739.97, 95% CI: 423.13, 3056.82) with low heterogeneity. From a societal perspective, AI-based DR screening was generally cost-effective (INB= 5102.33, 95% CI: -815.47, 11020.13), though the result lacked statistical significance and showed high heterogeneity. Conclusions: AI-based DR screening is generally cost-effective from a healthcare system perspective, particularly in high-income countries. Heterogeneity in cost-effectiveness across different perspectives highlights the importance of context-specific evaluations, to accurately evaluate the potential of AI-based DR screening in reducing global healthcare disparities.

Background: Diabetic retinopathy (DR) is a top leading cause of blindness worldwide, requiring early detection for timely intervention. Artificial Intelligence (AI) has emerged as a promising tool to improve DR screening efficiency, accessibility, and cost-effectiveness. This study conducted a systematic review of literature and meta-analysis on the economic outcomes of AI-based DR screening. Methods: A systematic review of studies published before September 2024 was conducted throughout PubMed, Scopus, Embase, the Cochrane Library, the National Health Service Economic Evaluation Database, and the Cost-Effectiveness Analysis Registry. Eligible studies were included if they were (1) conducted among type 1 diabetes mellitus or type 2 diabetes mellitus  adult diabetic population; (2) studies compared AI-based DR screening strategy to non-AI screening; and (3) performed a cost-effectiveness analysis. Meta-analysis was applied to pool incremental net benefit (INB) across studies stratified by country income and study perspective using a random-effects model. Statistical heterogeneity among studies was assessed using the I² statistic, Cochrane Q statistics, and meta regression. Results: Nine studies were included in the analysis. From a healthcare system/payer perspective, AI-based DR screening was significantly cost-effective compared to non-AI-based screening, with a pooled INB of 615.77 (95% confidence interval [CI]: 558.27, 673.27). Subgroup analysis showed robust cost-effectiveness of AI-based DR screening in high-income countries (INB = 613.62, 95% CI: 556.06, 671.18) and upper-/lower- middle income countries (INB = 1739.97, 95% CI: 423.13, 3056.82) with low heterogeneity. From a societal perspective, AI-based DR screening was generally cost-effective (INB= 5102.33, 95% CI: -815.47, 11020.13), though the result lacked statistical significance and showed high heterogeneity. Conclusions: AI-based DR screening is generally cost-effective from a healthcare system perspective, particularly in high-income countries. Heterogeneity in cost-effectiveness across different perspectives highlights the importance of context-specific evaluations, to accurately evaluate the potential of AI-based DR screening in reducing global healthcare disparities.