Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study

Artificial intelligence in pathologic myopia: a review of clinical research studies

Myopia is a significant global health challenge, with the incidence of pathologic myopia (PM) on the rise. PM-related fundus diseases have become a leading cause of irreversible blindness. Early detection and treatment are crucial for the prevention and control of myopia. Recent advancements in artificial intelligence (AI), particularly in machine learning and deep learning algorithms, have shown promising results in the field of PM in ophthalmology. This review explores the latest developments in AI technology for managing PM, emphasizing its role in screening and diagnosis, grading and classification, and predictive assessment. AI has shown significant potential for clinical application in PM management, enhancing its intelligent, precise, and efficient practices.

Adherence to the Canadian 24-hour movement guidelines and vision impairment in children and adolescents: a cross-sectional study

Objectives

To investigate the associations between adherence to the Canadian 24-hour movement guidelines-covering physical activity (PA), screen time (ST), and sleep duration (SD)-and vision impairment, specifically myopia and myopic anisometropia, among children and adolescents in Shenzhen, China.

Methods

A cross-sectional study was conducted in 2022 with 4,649 participants. Adherence to the guidelines was assessed using self-reported PA, ST, and SD measures, while vision impairment was clinically evaluated. Logistic regression models were used to analyze the associations, adjusting for sociodemographic factors.

Results

Among the participants, 48.63% were diagnosed with myopia and 11.01% had myopic anisometropia. Meeting the ST guideline was associated with a reduced risk of myopia (aOR = 0.86, 95% CI = 0.76-0.98) and myopic anisometropia (aOR = 0.78, 95% CI = 0.64-0.95). Meeting both PA and ST guidelines further reduced the odds of myopia (aOR = 0.73, 95% CI = 0.56-0.97) and myopic anisometropia (aOR = 0.60, 95% CI = 0.41-0.89). Meeting all three guidelines (PA, ST, and SD) significantly reduced the odds of myopia (aOR = 0.71, 95% CI: 0.53-0.93) and showed a trend toward reduced risk of anisometropia (aOR = 0.69, 95% CI: 0.47-1.02), compared to those who met none. Meeting two guidelines also significantly reduced the risk of myopia (aOR = 0.76, 95% CI: 0.59-0.97) and anisometropia (aOR = 0.71, 95% CI: 0.51-1.00).

Conclusion

Adherence to the 24-hour movement guidelines, particularly meeting the ST and PA recommendations, was associated with a lower risk of myopia and myopic anisometropia. These findings highlight the importance of promoting balanced lifestyle behaviors, such as limiting screen time and encouraging physical activity, to mitigate vision impairment among children and adolescents.

Analysis of the correlations between changes in posterior segment and anterior chamber segment after implantable collamer lens implantation in highly myopic patients

PURPOSE

To evaluate the impact of Implantable Collamer Lens (ICL) implantation on anterior chamber angle parameters and posterior segment structures in highly myopic eyes and explore potential correlations between these changes. The study aimed to assess alterations in superficial and deep vessel density (SVD, DVD), foveal avascular zone (FAZ) area, and retinal nerve fiber layer (RNFL) thickness to clarify the safety profile of ICL implantation.

METHODS

Prospective observational study, included 36 highly myopic eyes undergoing ICL implantation in surgery group and 23 non-surgical control eyes in non-surgery group. Anterior chamber parameters were assessed using AS-OCT, and posterior segment changes, including SVD, DVD, FAZ, and RNFL, were evaluated using OCT and OCTA preoperatively and at intervals up to 3 months postoperatively. Statistical analyses included paired t-tests, Wilcoxon tests, and Spearman correlation.

RESULTS

ICL implantation significantly improved uncorrected distance visual acuity (UDVA) (p < 0.01). Anterior chamber depth (ACD) and angle parameters, such as AOD and TISA, decreased initially but stabilized by 3 months. SVD and DVD showed early postoperative fluctuations, returning to baseline by 3 months, while the FAZ area and subfoveal choroidal thickness remained stable. Significant correlations were identified between anterior segment narrowing and posterior vascular changes, particularly in the pericentral region.

CONCLUSIONS

ICL implantation effectively and safely corrects high myopia, with stable anterior and posterior structural changes by 3 months. Transient vascular density fluctuations correlated with anterior chamber angle alterations, highlighting the need for long-term studies to further understand these dynamics and ensure retinal safety postoperatively.

TRIAL REGISTRATION

The study was approved by the ethics committee of Aier Eye Hospital, Jinan University (No. GZAIER2023IRB25; China Clinical Trial Record No. MR-44-24-009241).

Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis

BACKGROUND

In recent years, with the rapid development of machine learning (ML), it has gained widespread attention from researchers in clinical practice. ML models appear to demonstrate promising accuracy in the diagnosis of complex diseases, as well as in predicting disease progression and prognosis. Some studies have applied it to ophthalmology, primarily for the diagnosis of pathologic myopia and high myopia-associated glaucoma, as well as for predicting the progression of high myopia. ML-based detection still requires evidence-based validation to prove its accuracy and feasibility.

OBJECTIVE

This study aims to discern the performance of ML methods in detecting high myopia and pathologic myopia in clinical practice, thereby providing evidence-based support for the future development and refinement of intelligent diagnostic or predictive tools.

METHODS

PubMed, Cochrane, Embase, and Web of Science were thoroughly retrieved up to September 3, 2023. The prediction model risk of bias assessment tool was leveraged to appraise the risk of bias in the eligible studies. The meta-analysis was implemented using a bivariate mixed-effects model. In the validation set, subgroup analyses were conducted based on the ML target events (diagnosis and prediction of high myopia and diagnosis of pathological myopia and high myopia-associated glaucoma) and modeling methods.

RESULTS

This study ultimately included 45 studies, of which 32 were used for quantitative meta-analysis. The meta-analysis results unveiled that for the diagnosis of pathologic myopia, the summary receiver operating characteristic (SROC), sensitivity, and specificity of ML were 0.97 (95% CI 0.95-0.98), 0.91 (95% CI 0.89-0.92), and 0.95 (95% CI 0.94-0.97), respectively. Specifically, deep learning (DL) showed an SROC of 0.97 (95% CI 0.95-0.98), sensitivity of 0.92 (95% CI 0.90-0.93), and specificity of 0.96 (95% CI 0.95-0.97), while conventional ML (non-DL) showed an SROC of 0.86 (95% CI 0.75-0.92), sensitivity of 0.77 (95% CI 0.69-0.84), and specificity of 0.85 (95% CI 0.75-0.92). For the diagnosis and prediction of high myopia, the SROC, sensitivity, and specificity of ML were 0.98 (95% CI 0.96-0.99), 0.94 (95% CI 0.90-0.96), and 0.94 (95% CI 0.88-0.97), respectively. For the diagnosis of high myopia-associated glaucoma, the SROC, sensitivity, and specificity of ML were 0.96 (95% CI 0.94-0.97), 0.92 (95% CI 0.85-0.96), and 0.88 (95% CI 0.67-0.96), respectively.

CONCLUSIONS

ML demonstrated highly promising accuracy in diagnosing high myopia and pathologic myopia. Moreover, based on the limited evidence available, we also found that ML appeared to have favorable accuracy in predicting the risk of developing high myopia in the future. DL can be used as a potential method for intelligent image processing and intelligent recognition, and intelligent examination tools can be developed in subsequent research to provide help for areas where medical resources are scarce.

TRIAL REGISTRATION

PROSPERO CRD42023470820; https://tinyurl.com/2xexp738.

Cross-population validation of the PreMO risk indicator for predicting myopia onset in children

PURPOSE

The Predicting Myopia Onset and progression (PreMO) risk indicator, developed using data generated from white children in the UK, incorporates age, spherical equivalent refraction (SER), axial length (AL) and parental myopia to stratify the likelihood of developing myopia. This study evaluated the PreMO's predictive accuracy using prospective datasets from independent samples of children in Hong Kong (HK) and an ethnically diverse cohort of children in the United Kingdom.

METHODS

Non-myopic children (SER > -0.50 D) aged 6-8 and 9-10 years were scored using the PreMO risk indicator framework, integrating baseline cycloplegic SER, AL and parental myopia data. Scores were assigned risk categories as follows: 0 = no risk, 1-3 = low risk, 4-6 = moderate risk and 7-9 = high risk. SER at ≥15 years of age was used to define refractive outcomes as 'myopic' or 'not myopic'. PreMO's predictive accuracy was analysed via Receiver Operator Characteristic curves, with Youden's J-Index identifying the optimal risk score threshold. Sensitivity, specificity and area under the curve were determined and compared with those of singular predictors, that is, SER < +0.75 D and AL ≥ 23.07 mm at 6-8 years.

RESULTS

In the cohort of children aged 6-8 years, a PreMO risk score ≥ 4 exhibited high sensitivity in predicting myopia onset in UK (0.97) and HK (0.94) children, with high specificity in UK (0.96) and moderate specificity in HK (0.64) children. In UK children aged 6-8 years, the PreMO outperformed singular predictors such as SER and AL. Among HK children aged 9-10 years, the PreMO score maintained high sensitivity (0.90) and moderate specificity (0.72).

CONCLUSIONS

A PreMO risk score ≥ 4 is a strong predictive indicator for future myopia onset, particularly in UK children. Despite high sensitivity in both UK and HK cohorts, specificity varied, indicating the need for contextual application of the tool, particularly in pre-myopic Asian children.

Artificial intelligence in myopia in children: current trends and future directions

PURPOSE OF REVIEW

Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management.

RECENT FINDINGS

The bulk of artificial intelligence research in childhood myopia was previously focused on traditional machine learning models for the identification of children at high risk for myopia progression. Recently, there has been a surge of literature with larger datasets, more computational power, and more complex computation models, leveraging artificial intelligence for novel approaches including large-scale myopia screening using big data, multimodal data, and advancing imaging technology for myopia progression, and deep learning models for precision treatment.

SUMMARY

Artificial intelligence holds significant promise in transforming the field of childhood myopia management. Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy. However, along with these advancements in technology come practical challenges including regulation and clinical integration.

Epidemiology of Myopia: Prevalence, Risk Factors and Effects of Myopia

Myopia is the most common cause of visual impairment in children and young adults. In order to assess the consequences for society, it is necessary to know temporal trends in prevalence, incidence and associated factors in childhood and adolescence, as well as the rate of myopia progression, as based on epidemiological research. This paper presents a literature review of publications from 2020 up to March 2024, supplemented by other relevant publications. The prevalence of myopia in children and adolescents in Germany is almost stable and is significantly lower than in Asia. The development of myopia is influenced by outdoor activity, parental myopia, genetics and near work, while insufficient time spent outdoors in childhood is a significant and controllable risk factor for myopia-related complications such as myopic maculopathy, glaucoma, and retinal detachment.

Association Between Myopia and Pupil Diameter in Preschoolers: Evidence from a Machine Learning Approach Based on a Real-World Large-Scale Dataset

INTRODUCTION

Previous studies have explored the connections between various ocular biological parameters with myopia. Our previous study also found that pupil data can predict the myopic progression during the interventions for myopia. However, studies exploring the association between pupil diameter and myopia in preschoolers with myopia were lacking. Hence this study was aimed to investigate the association between pupil diameter and myopia in preschoolers with myopia based on a real-world, large-scale dataset.

METHODS

Data containing 650,671 preschoolers were collected from a total of 1943 kindergartens in Shenzhen, China. Refraction and pupil parameters were collected. After data filtering, the occurrence of myopia and its association with age, gender, pupil diameter, and other variables, were analyzed. Random forest (RF) and eXtreme gradient boosting (XGBoost) were selected from seven machine learning algorithms to build the model. The mean decrease accuracy (MDA), mean decrease Gini (MDG), and gain feature importance (GFI) techniques were employed to quantify the importance of pupil diameter and other features.

RESULTS

After the assessments, 51,325 valid records with complete pupil data were included, and 3468 (6.76%) were identified as myopia based on the calculated cycloplegic refraction. Preschoolers with myopia presented reduced pupil diameter and greater variation (5.00 ± 0.99 mm) compared to non-myopic preschoolers (6.22 ± 0.67 mm). A nonlinear relationship was found according to the scatterplots between pupil diameter and refraction (R2 = 0.14). Especially preschoolers with myopia had reduced pupil diameter compared to emmetropic preschoolers, but hyperope did not experience additional pupil enlargement. After adjusting for other covariates, this relationship is still consistent (P < 0.001). XGBoost and RF algorithms presented the highest performance and validated the importance of pupil diameter in myopia.

CONCLUSIONS

Based on a real-world large-scale dataset, the current study illuminated that preschoolers with myopia had a reduced pupil diameter compared to emmetropic preschoolers with a nonlinear pattern. Machine learning algorithms visualized and validated the pivotal role of pupil diameter in myopia.

TRIAL REGISTRATION

chictr.org Identifier: ChiCTR2200057391.

Study of myopia progression and risk factors in Hubei children aged 7-10 years using machine learning: a longitudinal cohort

BACKGROUND

To investigate the trend of refractive error among elementary school students in grades 1 to 3 in Hubei Province, analyze the relevant factors affecting myopia progression, and develop a model to predict myopia progression and the risk of developing high myopia in children.

METHODS

Longitudinal study. Using a cluster-stratified sampling method, elementary school students in grades 1 to 3 (15,512 in total) from 17 cities in Hubei Province were included as study subjects. Visual acuity, cycloplegic autorefraction, and height and weight measurements were performed for three consecutive years from 2019 to 2021. Basic information about the students, parental myopia and education level, and the students' behavioral habits of using the eyes were collected through questionnaires.

RESULTS

The baseline refractive errors of children in grades 1 ~ 3 in Hubei Province in 2019 were 0.20 (0.11, 0.27)D, -0.14 (-0.21, 0.06)D, and - 0.29 (-0.37, -0.22)D, respectively, and the annual myopia progression was - 0.65 (-0.74, -0.63)D, -0.61 (-0.73, -0.59)D and - 0.59 (-0.64, -0.51)D, with the prevalence of myopia increasing from 17.56%, 20.9%, and 34.08% in 2019 to 24.16%, 32.24%, and 40.37% in 2021 (Χ2 = 63.29, P < 0.001). With growth, children's refractive error moved toward myopia, and the quantity of myopic progression gradually diminished. (F = 291.04, P = 0.027). The myopia progression in boys was less than that in girls in the same grade (P < 0.001). The change in spherical equivalent refraction in myopic children was smaller than that in hyperopic and emmetropic children (F = 59.28, P < 0.001), in which the refractive change in mild myopia, moderate myopia, and high myopia children gradually increased (F = 73.12, P < 0.001). Large baseline refractive error, large body mass index, and high frequency of eating sweets were risk factors for myopia progression, while parental intervention and strong eye-care awareness were protective factors for delaying myopia progression. The nomogram graph predicted the probability of developing high myopia in children and found that baseline refraction had the greatest predictive value.

CONCLUSION

Myopia progression varies by age, sex, and myopia severity. Baseline refraction is the most important factor in predicting high myopia in childhood. we should focus on children with large baseline refraction or young age of onset of myopia in clinical myopia prevention and control.