Approximately 1 in 26 phakic adults in Singapore has MMD. Older age and myopic SE are major risk factors of MMD. Severe MMD has a substantial impact on visual impairment and functioning.

Background By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability.Methods In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China.Findings The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0•969 (95% CI 0•959-0•977) or higher for myopic macular degeneration and 0•913 (0•906-0•920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0•978 [0•957-0•994] for myopic macular degeneration and 0•973 [0•941-0•995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries.Interpretation Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine.Funding None.

Background/aimsTo evaluate the predictive performance of various predictors, including non-cycloplegic refractive error, for risk of myopia onset under pragmatic settings.MethodsThe Wenzhou Medical University Essilor Progression and Onset of Myopia Study is a prospective cohort study of schoolchildren aged 6–10 years from two elementary schools in Wenzhou, China. Non-cycloplegic refraction, ocular biometry and accommodation measurements were performed. Myopia was defined as spherical equivalent (SE) ≤−0.5 diopter (D). ORs using multivariable logistic regression were determined. Area under the curve (AUC) evaluation for predictors was performed.ResultsSchoolchildren who attended both baseline and 2-year follow-up were analysed (N=1022). Of 830 non-myopic children at baseline, the 2-year incidence of myopia was 27.6% (95% CI, 24.2% to 31.3%). Female gender (OR=2.2), more advanced study grades (OR=1.5), less hyperopic SE (OR=11.5 per D), longer axial length (AL; OR=2.3 per mm), worse presenting visual acuity (OR=2.3 per decimal), longer near work time (OR=1.1 per hour/day) and lower magnitude of positive relative accommodation (PRA; OR=1.4 per D) were associated with myopia onset. PRA (AUC=0.66), SE (AUC=0.64) and AL (AUC=0.62) had the highest AUC values. The combination of age, gender, parental myopia, SE, AL and PRA achieved an AUC of 0.74.ConclusionApproximately one in four schoolchildren had myopia onset over a 2-year period. The predictors of myopia onset include lower magnitude of PRA, less hyperopic SE, longer AL and female gender. Of these, non-cycloplegic SE and PRA were the top single predictors, which can facilitate risk profiling for myopia onset.

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PurposeTo investigate the characteristics, risk factors and visual impact of myopic traction maculopathy (MTM) among adults with myopia in Singapore.MethodsWe analysed 3316 myopic eyes of adults aged over 40 years who participated in the Singapore Epidemiology of Eye Diseases-2 study. Detailed questionnaires and ophthalmic examinations were conducted. A total of 2913 myopic eyes of 1639 subjects were graded for MTM by spectral-domain optical coherence tomography. MTM is defined as the presence of retinoschisis, lamellar or full-thickness macula hole and foveal retinal detachment. Fundus photographs were graded for myopic macular degeneration (MMD).ResultsOf these 2913 myopic eyes, the mean and SD of age was 60.1±8.0 years; the spherical equivalent (SE) was −2.5±2.3 D; and the axial length (AL) was 24.6±1.3 mm. MTM was found in 0.9% of myopic eyes and 7.3% of highly myopic eyes. In the multivariate analysis, myopic SE (p<0.001), longer AL (p<0.001), MMD (p=0.01) and epiretinal traction (p<0.001) were independent risk factors for MTM. MTM was not associated with age (p=0.38). MTM was significantly associated with poorer best-corrected visual acuity (BCVA) (p<0.01).ConclusionsOur population-based study revealed that MTM was present in 0.9% of myopic eyes and 7.3% of highly myopic eyes. While greater myopic SE, longer AL, MMD and epiretinal traction are risk factors of MTM, age was not related to MTM. MTM has a negative effect on BCVA.

The aim of this study was to investigate the agreement between cycloplegic and non-cycloplegic autorefraction with an open-field auto refractor in a school vision screening set up, and to define a threshold for myopia that agrees with the standard cycloplegic refraction threshold. The study was conducted as part of the Sankara Nethralaya Tamil Nadu Essilor Myopia (STEM) study, which investigated the prevalence, incidence, and risk factors for myopia among children in South India. Children from two schools aged 5 to 15 years, with no ocular abnormalities and whose parents gave informed consent for cycloplegic refraction were included in the study. All the children underwent visual acuity assessment (Pocket Vision Screener, Elite school of Optometry, India), followed by non-cycloplegic and cycloplegic (1% tropicamide) open-field autorefraction (Grand Seiko, WAM-5500). A total of 387 children were included in the study, of whom 201 were boys. The mean (SD) age of the children was 12.2 (±2.1) years. Overall, the mean difference between cycloplegic and non-cycloplegic spherical equivalent (SE) open-field autorefraction measures was 0.34 D (limits of agreement (LOA), 1.06 D to −0.38 D). For myopes, the mean difference between cycloplegic and non-cycloplegic SE was 0.13 D (LOA, 0.63D to −0.36D). The prevalence of myopia was 12% (95% CI, 8% to 15%) using the threshold of cycloplegic SE ≤ −0.50 D, and was 14% (95% CI, 11% to 17%) with SE ≤ −0.50 D using non-cycloplegic refraction. When myopia was defined as SE of ≤−0.75 D under non-cycloplegic conditions, there was no difference between cycloplegic and non-cycloplegic open-field autorefraction prevalence estimates (12%; 95% CI, 8% to 15%; p = 1.00). Overall, non-cycloplegic refraction underestimates hyperopia and overestimates myopia; but for subjects with myopia, this difference is minimal and not clinically significant. A threshold of SE ≤ −0.75 D agrees well for the estimation of myopia prevalence among children when using non-cycloplegic refraction and is comparable with the standard definition of cycloplegic myopic refraction of SE ≤ −0.50 D.

To report the baseline prevalence of myopia among school children in Tamil Nadu, South India from a prospective cohort study. Methods: Children between the ages of 5 and 16 years from 11 schools in two districts of Tamil Nadu underwent vision screening. All children underwent visual acuity assessment using a Pocket Vision Screener followed by non-cycloplegic open-field autorefraction (Grand Seiko WAM-5500). Myopia was defined as a spherical equivalent (SE) refraction of ≤−0.75 D and high myopia was defined as SE ≤ −6.00 D. Distribution of refraction, biometry and factors associated with prevalence of myopia were the outcome measures.Results: A total of 14,699 children completed vision screening, with 2% (357) of them having ocular abnormalities other than refractive errors or poor vision despite spectacle correction. The remaining 14,342 children (7557 boys; 52.69%) had a mean age of 10.2 (Standard Deviation [SD] 2.8) years. A total of 2502 had myopia in at least one eye, a prevalence of 17.5% (95% CI: 14.7-20.5%), and 74 (0.5%; 95% CI: 0.3-0.9%) had high myopia. Myopia prevalence increased with age (p < 0.001), but sex was not associated with myopia prevalence (p = 0.24). Mean axial length (AL; 23.08 (SD = 0.91) mm) and mean anterior chamber depth (ACD; 3.45 (SD = 0.27) mm) positively correlated with age (p < 0.001). The mean flat (K1; 43.37 (SD = 1.49) D) and steep (K2; 44.50 (SD = 1.58) D) corneal curvatures showed negative correlation with age (p = 0.02 and p < 0.001, respectively). In the multivariable logistic regression, older age and urban school location had higher odds for prevalence of myopia. Conclusion:The baseline prevalence of myopia among 5-to 16-year-old children in South India is larger than that found in previous studies, indicating that myopia is becoming a major public health problem in this country.

AimsTo determine the prevalence and predictors of myopic macular degeneration (MMD) in a consortium of Asian studies.MethodsIndividual-level data from 19 885 participants from four population-based studies, and 1379 highly myopic participants (defined as axial length (AL) >26.0 mm) from three clinic-based/school-based studies of the Asian Eye Epidemiology Consortium were pooled. MMD was graded from fundus photographs following the meta-analysis for pathologic myopia classification and defined as the presence of diffuse choroidal atrophy, patchy chorioretinal atrophy, macular atrophy, with or without ‘plus’ lesion (lacquer crack, choroidal neovascularisation or Fuchs’ spot). Area under the curve (AUC) evaluation for predictors was performed for the population-based studies.ResultsThe prevalence of MMD was 0.4%, 0.5%, 1.5% and 5.2% among Asians in rural India, Beijing, Russia and Singapore, respectively. In the population-based studies, older age (per year; OR=1.13), female (OR=2.0), spherical equivalent (SE; per negative diopter; OR=1.7), longer AL (per mm; OR=3.1) and lower education (OR=1.9) were associated with MMD after multivariable adjustment (all p<0.001). Similarly, in the clinic-based/school-based studies, older age (OR=1.07; p<0.001), female (OR=2.1; p<0.001), longer AL (OR=2.1; p<0.001) and lower education (OR=1.7; p=0.005) were associated with MMD after multivariable adjustment. SE had the highest AUC of 0.92, followed by AL (AUC=0.87). The combination of SE, age, education and gender had a marginally higher AUC (0.94).ConclusionIn this pooled analysis of multiple Asian studies, older age, female, lower education, greater myopia severity and longer AL were risk factors of MMD, and myopic SE was the strongest single predictor of MMD.