Factors influencing myopia stabilisation in young myopic adult Singaporeans.

PURPOSE: To evaluate factors influencing stabilisation of myopia in the Singapore Cohort of Risk factors for Myopia. METHODS: We evaluated the longitudinal natural history of 424 myopic participants from 1999 to 2022. The outcome was the change in myopia from the adolescence follow-up visit (aged 12-19 years) to the adulthood follow-up visit (aged 26-33 years). Association of predictive factors, including baseline spherical error, gender, ethnicity, parental myopia, time outdoor, near work and age at adolescence, was examined with the dichotomous outcome of adult myopia progression (≤ -1.00 dioptres (D) over 10 years) using multiple logistic regression and progression in linear regression models. RESULTS: For the primary outcome, the mean rate of progression of the outcome was found to be -0.04±0.09 D per year from the adolescent to the adulthood follow-up visits. 82.3% (95% CI 78.3% to 85.8%) had myopia stabilisation, with progression of less than 1.00 D over 10 years while 61.3% (95% CI 56.5% to 66.0%) of the subjects had progression of less than 0.50 D. In logistic regression models, both male gender (p=0.035) and non-Chinese ethnicity (p=0.032) were more likely to achieve myopia stabilisation while in linear multivariate regression models, males had a significantly slower degree of myopia progression (p=0.021). CONCLUSION: 5 in 6 Singaporean young adults had myopia stabilisation. Male gender is 2 times and non-Chinese ethnicities are 2.5 times more likely to achieve myopia stabilisation. However, a proportion of myopes continue to exhibit a clinically significant degree of progression in adulthood.

Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children.

Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in primary dataset, 0.97 versus 0.94 in test dataset; mixed model AUC 0.99 versus 0.97 in primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical-decision support tool to identify "at-risk" children for early intervention.

Development and Validation of a Model to Predict Who Will Develop Myopia in the Following Year as a Criterion to Define Premyopia.

PURPOSE: To develop and validate models to predict who will develop myopia in the following year based on cycloplegic refraction or ocular biometry and to identify thresholds of premyopia. METHODS: Prospective longitudinal data of nonmyopic children at baseline from the Guangzhou Twins Eye Study and the Guangzhou Outdoor Activity Longitudinal Study were used as the training set, and the Singapore Cohort Study of the Risk factors for Myopia study formed the external validation set. Age, sex, cycloplegic refraction, ocular biometry, uncorrected visual acuity, and parental myopia were integrated into 3 logistic regression models to predict the onset of myopia in the following year. Premyopia cutoffs and an integer risk score system were derived based on the identified risk. RESULTS: In total, 2896 subjects with at least 2 visits were included. Cycloplegic refraction at baseline is a better predictor to identify the children with myopia onset [C-statistic=0.91, 95% confidence interval (CI), 0.87-0.94; C-statistic=0.92, 95% CI, 0.92-0.92 for internal and external validation, respectively], comparing to axial length, corneal curvature radius (CR) and anterior chamber depth (C-statistic=0.81, 95% CI, 0.73-0.88; C-statistic=0.80, 95% CI, 0.79-0.80, respectively), and axial length/CR (C-statistic=0.78, 95% CI, 0.71-0.85; C-statistic=0.76, 95% CI, 0.75-0.76). With a risk of >70%, the definitions of premyopia indicating approaching myopia onset were 0.00 D for 6-8 years and -0.25 D for ≥9 years in children with 2 myopic parents. CONCLUSIONS: Either cycloplegic refraction or ocular biometry can predict 1-year risk of myopia. Premyopia can be successfully defined through risk assessments based on children's age and predict who would require more aggressive myopia prophylaxis.

Predictors of myopic macular degeneration in a 12-year longitudinal study of Singapore adults with myopia.

PURPOSE: To investigate the predictive factors for myopic macular degeneration (MMD) and progression in adults with myopia. METHODS: We examined 828 Malay and Indian adults (1579 myopic eyes) with myopia (spherical equivalent (SE) ≤-0.5 dioptres) at baseline who participated in both baseline and 12-year follow-up visits of the Singapore Malay Eye Study and the Singapore Indian Eye Study. Eye examinations, including subjective refraction and axial length (AL) measurements, were performed. MMD was graded from fundus photographs following the Meta-Analysis for Pathologic Myopia classification. The predictive factors for MMD development and progression were assessed in adults without and with MMD at baseline, respectively as risk ratios (RR) using multivariable modified Poisson regression models. The receiver operating characteristic curve was used to visualise the performance of the predictive models for the development of MMD, with performance quantified by the area under the curve (AUC). RESULTS: The 12-year cumulative MMD incidence was 10.3% (95% CI 8.9% to 12.0%) among 1504 myopic eyes without MMD at baseline. Tessellated fundus was a major predictor of MMD (RR=2.50, p<0.001), among other factors including age, worse SE and longer AL (all p<0.001). The AUC for prediction of MMD development was found to be 0.78 (95% CI 0.76 to 0.80) for tessellated fundus and increased significantly to an AUC of 0.86 (95% CI 0.84 to 0.88) with the combination of tessellated fundus with age, race, gender and SE (p<0.001). Older age (p=0.02), worse SE (p<0.001) and longer AL (p<0.001) were found to be predictors of MMD progression. CONCLUSIONS: In adults with myopia without MMD, tessellated fundus, age, SE and AL had good predictive value for incident MMD. In adults with MMD, 1 in 10 eyes experienced progression over the same period. Older age, more severe myopia and longer AL were independent risk factors for progression.

Pathologic myopia: advances in imaging and the potential role of artificial intelligence.

Pathologic myopia is a severe form of myopia that can lead to permanent visual impairment. The recent global increase in the prevalence of myopia has been projected to lead to a higher incidence of pathologic myopia in the future. Thus, imaging myopic eyes to detect early pathological changes, or predict myopia progression to allow for early intervention, has become a key priority. Recent advances in optical coherence tomography (OCT) have contributed to the new grading system for myopic maculopathy and myopic traction maculopathy, which may improve phenotyping and thus, clinical management. Widefield fundus and OCT imaging has improved the detection of posterior staphyloma. Non-invasive OCT angiography has enabled depth-resolved imaging for myopic choroidal neovascularisation. Artificial intelligence (AI) has shown great performance in detecting pathologic myopia and the identification of myopia-associated complications. These advances in imaging with adjunctive AI analysis may lead to improvements in monitoring disease progression or guiding treatments. In this review, we provide an update on the classification of pathologic myopia, how imaging has improved clinical evaluation and management of myopia-associated complications, and the recent development of AI algorithms to aid the detection and classification of pathologic myopia.

Association of time outdoors and patterns of light exposure with myopia in children.

BACKGROUND/AIMS: To evaluate the association of reported time outdoors and light exposure patterns with myopia among children aged 9 years from the Growing Up in Singapore Towards Healthy Outcomes birth cohort. METHODS: We assessed reported time outdoors (min/day), light exposure patterns and outdoor activities of children aged 9 years (n=483) with a questionnaire, the FitSight watch and a 7-day activity diary. Light levels, the duration, timing and frequency of light exposure were assessed. Cycloplegic spherical equivalent (SE), myopia (SE≤-0.5 D) and axial length (AL) of paired eyes were analysed using generalised estimating equations. RESULTS: In this study, 483 (966 eyes) multiethnic children (50.0% boys, 59.8% Chinese, 42.2% myopic) were included. Reported time outdoors (mean±SD) was 100±93 min/day, and average light levels were 458±228 lux. Of the total duration children spent at light levels of ≥1000 lux (37±19 min/day), 76% were spent below 5000 lux. Peak light exposure occurred at mid-day. Children had 1.7±1.0 light exposure episodes/day. Common outdoor activities were walks, neighbourhood play and swimming. Greater reported time outdoors was associated with lower odds of myopia (OR=0.82, 95% CI 0.70 to 0.95/hour increase daily; p=0.009). Light levels, timing and frequency of light exposures were not associated with myopia, SE or AL (p>0.05). CONCLUSION: Reported time outdoors, light levels and number of light exposure episodes were low among Singaporean children aged 9 years. Reported time outdoors was protective against myopia but not light levels or specific light measures. A multipronged approach to increase time outdoors is recommended in the combat against the myopia epidemic.

Systematic Review and Meta-Analysis on the Impact of COVID-19 Pandemic-Related Lifestyle on Myopia.

PURPOSE: To conduct a systematic review and meta-analysis to assess the effects of coronavirus disease 2019 (COVID-19) pandemic-related lifestyle on myopia outcomes in children to young adults. METHODS: A systematic search was conducted on PubMed, Embase, and the Cochrane Central Register of Controlled Trials databases (with manual searching of reference lists of reviews). Studies included assessed changes in myopia-related outcomes (cycloplegic refraction) during COVID and pre-COVID. Of 367 articles identified, 7 (6 prospective cohorts; 1 repeated cross-sectional study) comprising 6327 participants aged 6 to 17 were included. Quality appraisals were performed with Joanna Briggs Institute Critical Appraisal Checklists. Pooled differences in annualized myopic shifts or mean spherical equivalent (SE) during COVID and pre-COVID were obtained from random-effects models. RESULTS: In all 7 studies, SE moved toward a myopic direction during COVID (vs pre-COVID), where 5 reported significantly faster myopic shifts [difference in means of changes: -1.20 to -0.35 diopters per year, [D/y]; pooled estimate: -0.73 D/y; 95% confidence interval (CI): -0.96, -0.50; P<0.001], and 2 reported significantly more myopic SE (difference in means: -0.72 to -0.44 D/y; pooled estimate: -0.54 D/y; 95% CI: -0.80, -0.28; P<0.001). Three studies reported higher myopia (SE ≤-0.50 D) incidence (2.0- to 2.6-fold increase) during COVID versus pre-COVID. Of studies assessing lifestyle changes, all 4 reported lower time outdoors (pre-COVID vs during COVID: 1.1-1.8 vs 0.4-1.0 hours per day, [h/d]), and 3 reported higher screen time (pre-COVID vs during COVID: 0.7-2.8 vs 2.4-6.9 h/d). CONCLUSIONS: This review suggests more myopic SE shifts during COVID (vs pre-COVID) in participants aged 6 to 17. COVID-19 restrictions may have worsened SE shifts, and lifting of restrictions may lessen this effect. Evaluations of the long-term effects of the pandemic lifestyle on myopia onset and progression in large studies are warranted to confirm these findings.

Advances in OCT Imaging in Myopia and Pathologic Myopia.

Advances in imaging with optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) technology, including the development of swept source OCT/OCTA, widefield or ultra-widefield systems, have greatly improved the understanding, diagnosis, and treatment of myopia and myopia-related complications. Anterior segment OCT is useful for imaging the anterior segment of myopes, providing the basis for implantable collamer lens optimization, or detecting intraocular lens decentration in high myopic patients. OCT has enhanced imaging of vitreous properties, and measurement of choroidal thickness in myopic eyes. Widefield OCT systems have greatly improved the visualization of peripheral retinal lesions and have enabled the evaluation of wide staphyloma and ocular curvature. Based on OCT imaging, a new classification system and guidelines for the management of myopic traction maculopathy have been proposed; different dome-shaped macula morphologies have been described; and myopia-related abnormalities in the optic nerve and peripapillary region have been demonstrated. OCTA can quantitatively evaluate the retinal microvasculature and choriocapillaris, which is useful for the early detection of myopic choroidal neovascularization and the evaluation of anti-vascular endothelial growth factor therapy in these patients. In addition, the application of artificial intelligence in OCT/OCTA imaging in myopia has achieved promising results.

Sleep Patterns and Myopia Among School-Aged Children in Singapore.

Purpose: To evaluate the associations of sleep factors with myopia, spherical equivalent (SE), and axial length (AL) in elementary school-aged children from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort. Methods: This cross-sectional study included multi-ethnic children who participated in the GUSTO prospective birth cohort and were delivered in two major tertiary hospitals in Singapore (2009-2010). Sleep factors and myopia outcomes were assessed at the 8- and 9-year study visits, respectively. Parent-reported sleep quality was assessed with the Children's Sleep Habits Questionnaire (CSHQ) total scores. Additionally, each child's sleep duration, timing (bedtime; waketime), and the consistency of sleep duration or timing (i.e., the difference between weekends and weekdays) were parent-reported. Outcomes included cycloplegic SE, myopia (SE ≤ -0.5 D) and AL. Eye measurements from both eyes were included in the analyses. Multivariable linear or logistic regression with Generalized Estimating Equations were used to account for the correlation between paired eyes and confounders in the associations of sleep factors at age 8 and myopia at age 9. Results: A total of 572 multi-ethnic children (49.5% boys; 56.1% Chinese) aged 9 years were included in the analyses. Overall, 37.3% of eyes were myopic. Children reported a mean total CSHQ score of 46 [standard deviation (SD) = 6]. The mean duration of sleep was 9.2 (SD = 1.0) hours per day (h/day), with 59.9% of children reporting sufficient sleep (≥9 h/day) based on guidelines recommended by the National Sleep Foundation, USA. The mean bedtime and wake time were 22:00 (SD = 00:53) and 07:08 (SD = 00:55), respectively. In multivariable regression models, total CSHQ scores, the duration of sleep, bedtime and wake time were not significantly associated with myopia, SE, or AL (p ≥ 0.05 for all), adjusting for gender, ethnicity, time outdoors, near-work, parental myopia, maternal education levels (and additionally the child's height when the outcome was AL). Similarly, the consistency of both the duration and timing of sleep (across weekends and weekdays) were not significantly associated with myopia, SE, or AL (p ≥ 0.05 for all). Conclusion: In this cross-sectional study, sleep quality, duration, timing, and the consistency of specific sleep factors were not independently associated with myopia, SE, or AL among elementary school-aged children in Singapore. Large longitudinal studies are warranted to corroborate these results.

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology.

PURPOSE OF REVIEW: The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS: Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY: AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

The Potential of Current Polygenic Risk Scores to Predict High Myopia and Myopic Macular Degeneration in Multiethnic Singapore Adults.

PURPOSE: To evaluate the transancestry portability of current myopia polygenic risk scores (PRSs) to predict high myopia (HM) and myopic macular degeneration (MMD) in an Asian population. DESIGN: Population-based study. PARTICIPANTS: A total of 5894 adults (2141 Chinese, 1913 Indian, and 1840 Malay) from the Singapore Epidemiology of Eye Diseases study were included in the analysis. The mean ± standard deviation age was 57.05 ± 9.31 years. A total of 361 adults had a diagnosis of HM (spherical equivalent [SE] < -5.00 diopters [D]) from refraction measurements, 240 individuals had a diagnosis of MMD graded by the International Photographic Classification and Grading System for Myopic Maculopathy criteria from fundus photographs, and 3774 individuals were control participants without myopia (SE > -0.5 D). METHODS: The PRS, derived from 687 289 HapMap3 single nucleotide polymorphisms (SNPs) from the largest genome-wide association study of myopia in Europeans to date (n = 260 974), was assessed on its ability to predict patients with HM and MMD versus control participants. MAIN OUTCOME MEASURES: The primary outcomes were the area under the receiver operating characteristic curve (AUC) to predict HM and MMD. RESULTS: The PRS had an AUC of 0.73 (95% confidence interval [CI], 0.70-0.75) for HM and 0.66 (95% CI, 0.63-0.70) for MMD versus no myopia. The inclusion of the PRS with other predictors (age, sex, educational attainment [EA], and ancestry; age-by-ancestry, sex-by-ancestry, and EA-by-ancestry interactions; and 20 genotypic principal components) increased the AUC to 0.84 (95% CI, 0.82-0.86) for HM and 0.79 (95% CI, 0.76-0.82) for MMD. Individuals with a PRS in the top 5% showed up to a 4.66 (95% CI, 3.34-6.42) times higher risk of HM developing and up to a 3.43 (95% CI, 2.27-5.05) times higher risk of MMD developing compared with the remaining 95% of individuals. CONCLUSIONS: The PRS is a good predictor for HM and facilitates the identification of high-risk children to prevent myopia progression to HM. In addition, the PRS also predicts MMD and helps to identify high-risk adults with myopia who require closer monitoring for myopia-related complications.

Application of artificial intelligence in cataract management: current and future directions.

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract  is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.

Dietary intake and associations with myopia in Singapore children.

PURPOSE: To evaluate associations of dietary factors with myopia, spherical equivalent refractive error (SE) and axial length (AL) in children at age 9 from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort. METHODS: We included 467 multi-ethnic children (933 eyes) who participated in the GUSTO prospective birth cohort and were delivered in two major hospitals in Singapore (2009-2010). At the 9-year visit, we assessed the 6-year incidence of myopia (between ages 3 to 9), cycloplegic SE and AL in children without myopia (SE ≤ -0.5 D in either eye) at the 3-year visit. Using a validated 112-item food frequency questionnaire, parents reported each child's average daily intake of dietary factors (nutrients and food groups) in the past month. Paired eyes were analysed using Generalised Estimating Equations with multivariable logistic or linear regression. Bonferroni corrections were applied, correcting for multiple comparisons between the 13 nutrients (p < 0.004) or 8 food groups (p < 0.006) and each outcome. RESULTS: In children aged 9 years (51.0% boys; 56.3% Chinese), the 6-year incidence of myopia was 35.5%. Overall, the mean (SD) SE and AL were -0.3 (1.7) D and 23.4 (1.0) mm, respectively. In multivariable regression, macronutrients or micronutrients were not associated with incident myopia (p ≥ 0.004 for all), adjusting for total energy, gender, ethnicity, time outdoors, near-work and the number of myopic parents (additionally child's height for outcome AL). Similarly, all food groups (including refined grains, sugar-sweetened beverages, protein foods, fruits and vegetables) were not associated with incident myopia (p ≥ 0.006 for all). Additionally, none of the nutrients (p ≥ 0.004 for all) or food groups (p ≥ 0.006 for all) were associated with SE or AL. CONCLUSIONS: Our study findings of no significant association between specific nutrients or food groups and incident myopia or SE or AL suggest that diet may not be associated with myopia in children aged 9 years. Well-conducted prospective studies in other populations may clarify the association.

Cost of Myopia Correction: A Systematic Review.

Myopia is one of the leading causes of visual impairment globally. Despite increasing prevalence and incidence, the associated cost of treatment remains unclear. Health care spending is a major concern in many countries and understanding the cost of myopia correction is the first step eluding to the overall cost of myopia treatment. As cost of treatment will reduce the burden of cost of illness, this will aid in future cost-benefit analysis and the allocation of healthcare resources, including considerations in integrating eye care (refractive correction with spectacles) into universal health coverage (UHC). We performed a systematic review to determine the economic costs of myopia correction. However, there were few studies for direct comparison. Costs related to myopia correction were mainly direct with few indirect costs. Annual prevalence-based direct costs for myopia ranged from $14-26 (USA), $56 (Iran) and $199 (Singapore) per capita, respectively (population: 274.63 million, 75.15 million and 3.79 million, respectively). Annually, the direct costs of contact lens were $198.30-$378.10 while spectacles and refractive surgeries were $342.50 and $19.10, respectively. This review provides an insight to the cost of myopia correction. Myopia costs are high from nation-wide perspectives because of the high prevalence of myopia, with contact lenses being the more expensive option. Without further interventions, the burden of illness of myopia will increase substantially with the projected increase in prevalence worldwide. Future studies will be necessary to generate more homogenous cost data and provide a complete picture of the global economic cost of myopia.

Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions.

PURPOSE OF REVIEW: Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still in the formative stages of development in healthcare, with promising applications and potential challenges in its applications. This review provides an overview of AI-based NLP, its applications in healthcare and ophthalmology, next-generation use case, as well as potential challenges in deployment. RECENT FINDINGS: The integration of AI-based NLP systems into existing clinical care shows considerable promise in disease screening, risk stratification, and treatment monitoring, amongst others. Stakeholder collaboration, greater public acceptance, and advancing technologies will continue to shape the NLP landscape in healthcare and ophthalmology. SUMMARY: Healthcare has always endeavored to be patient centric and personalized. For AI-based NLP systems to become an eventual reality in larger-scale applications, it is pertinent for key stakeholders to collaborate and address potential challenges in application. Ultimately, these would enable more equitable and generalizable use of NLP systems for the betterment of healthcare and society.

New Polygenic Risk Score to Predict High Myopia in Singapore Chinese Children.

Purpose: The purpose of this study was to develop an Asian polygenic risk score (PRS) to predict high myopia (HM) in Chinese children in the Singapore Cohort of Risk factors for Myopia (SCORM) cohort. Methods: We included children followed from 6 to 11 years old until teenage years (12-18 years old). Cycloplegic autorefraction, ultrasound biometry, Illumina HumanHap 550, or 550 Duo Beadarrays, demographics, and environmental factors data were obtained. The PRS was generated from the Consortium for Refractive Error and Myopia genomewide association study (n = 542,934) and the Strabismus, Amblyopia, and Refractive Error in Singapore children Study (n = 500). The Growing Up in Singapore Towards healthy Outcomes Cohort study (n = 339) was the replication cohort. The outcome was teenage HM (≤ -5.00 D) with predictive performance assessed using the area under the curve (AUC). Results: Mean baseline age ± SD was 7.85 ± 0.84 (n = 1004) and 571 attended the teenage visit; 23.3% had HM. In multivariate analysis, the PRS was associated with a myopic spherical equivalent with an incremental R2 of 0.041 (95% confidence interval [CI] = 0.010, 0.073; P < 0.001). AUC for HM (0.77 [95% CI = 0.71-0.83]) performed better (P = 0.02) with the PRS compared with a model without (0.72 [95% CI = 0.65, 0.78]). Children at the top 25% PRS risk had a 2.34-fold-greater risk of HM (95% CI = 1.53, 3.55; P < 0.001). Conclusions: The new Asian PRS improved the predictive performance to detect children at risk of HM. Translational Relevance: Clinicians may use the PRS with other predictive factors to identify high risk children and guide interventions to reduce the risk of HM later in life.

Artificial intelligence in myopia: current and future trends.

PURPOSE OF REVIEW: Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management. RECENT FINDINGS: There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage. SUMMARY: Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.

Rapid Myopic Progression in Childhood Is Associated With Teenage High Myopia.

Purpose: The purpose of this study was to evaluate the association of childhood progression of spherical equivalent (SE) with high myopia (HM) in teenagers in the Singapore Cohort of Risk factors for Myopia (SCORM). Methods: We included 928 SCORM children followed over a mean follow-up of 6.9 ± 1.0 years from baseline (6-11 years old) until their teenage years (12-19 years old). Cycloplegic autorefraction and axial length (AL) measurements were performed yearly. The outcomes in teenagers were HM (SE ≤ -5 diopter [D)], AL ≥ 25 mm, SE and AL. Three-year SE and AL progression in childhood and baseline SE and AL with outcomes were evaluated using multivariable logistic or linear regression models, with predictive performance of risk factors assessed using the area under the curve (AUC). Results: At the last visit, 9.8% of teenagers developed HM and 22.7% developed AL ≥ 25 mm. In multivariate regression analyses, every -0.3 D/year increase in 3-year SE progression and every 0.2 mm/year increase in 3-year AL progression were associated with a -1.14 D greater teenage SE and 0.52 mm greater teenage AL (P values < 0.001). The AUC (95% confidence interval [CI]) of a combination of 3-year SE progression and baseline SE for teenage HM was 0.97 (95% CI = 0.95 - 0.98). The AUC of 3-year AL progression and baseline AL for teenage AL ≥ 25 mm was 0.91 (95% CI = 0.89 - 0.94). Conclusions: Three-year myopia progression in childhood combined with baseline SE or AL were good predictors of teenage HM. Clinicians may use this combination of factors to guide timing of interventions, potentially reducing the risk of HM later in life.