Objectives: The aim of this study was to assess the association between axial length/corneal radius ratio (AL/CR ratio), AL, and refractive status and evaluate the accuracy of AL and AL/CR ratio for myopia assessment among Chinese children.Methods: A diagnostic trial was conducted in Shenzhen Eye Hospital from June 2020 to December 2020. Cycloplegic refraction and demographic characteristic survey were carried out, and AL and CR were measured. The Pearson correlation analysis between AL, AL/CR ratio, and spherical equivalent (SE) was carried out. The sensitivity, specificity, Youden index, positive predictive value, and negative predictive value of the AL/CR ratio and AL for myopia assessment were analyzed using cycloplegic refraction as the gold standard by drawing receiver operating characteristic (ROC) curves.Results: A total of 300 children aged 8–18 years participated in this study. The Pearson correlation coefficient between AL and SE was −0.667 (P < 0.05) and −0.754 (P < 0.05) between AL/CR ratio and SE. There were significant differences in SE, AL, and AL/CR ratio among different age groups (p < 0.05). SE decreased by 1.185 diopter (D) for every 1 mm increase in AL and decreased by 0.667 D for every 0.1 increase in the AL/CR ratio. Taking cycloplegic refraction SE ≤ −0.50 D as the gold standard for the diagnosis of myopia, the area under the ROC curve of AL for myopia assessment was 0.836 (95% confidence interval [CI]: 0.767–0.906), with specificity, sensitivity, and Youden index of 0.833, 0.767, and 0.600, respectively. The area under the ROC curve of AL/CR ratio for myopia assessment was 0.937 (95% CI: 0.878–0.996), with specificity, sensitivity, Youden index, positive predictive value, and negative predictive value of 0.703, 0.913, 0.622, 0.956, and 0.771, respectively. The area under the ROC curve of the combination of AL/CR ratio and parental myopia for myopia assessment was 0.976 (95% CI: 0.957–0.996).Conclusion: The correlation between SE and AL/CR ratio was stronger than that between SE and AL in children. The AL/CR ratio may be an alternative indicator for myopia assessment in children, and the combination of demographic factors and AL/CR ratio can improve the accuracy of myopia assessment.

Background: Patients with high myopia have an increased lifetime risk of complications. The prevalence patterns of high myopia in children and adolescents in southern China are unclear. Early identification of high-risk individuals is critical for reducing the occurrence and development of high myopia and avoiding the resulting complications. Objective: This study aimed to determine the prevalence of high myopia in children and adolescents in southern China via real-world screening data and to predict its onset by studying the risk factors for high myopia based on machine learning. Methods: This retrospective school-based study was conducted in 13 cities with different gross domestic products in southern China. Through data acquisition and filtering, we analyzed the prevalence of high myopia and its association with age, school stage, gross domestic product, and risk factors. A random forest algorithm was used to predict high myopia among schoolchildren and then assessed in an independent hold-out group. Results: There were 1,285,609 participants (mean age 11.80, SD 3.07, range 6-20 years), of whom 658,516 (51.2%) were male. The overall prevalence of high myopia was 4.48% (2019), 4.88% (2020), and 3.17% (2021), with an increasing trend from the age of 11 to 17 years. The rates of high myopia increased from elementary schools to high schools but decreased at all school stages from 2019 to 2021. The coastal and southern cities had a higher proportion of high myopia, with an overall prevalence between 2.60% and 5.83%. Age, uncorrected distance visual acuity, and spherical equivalents were predictive factors for high myopia onset in schoolchildren. The random forest algorithm achieved a high accuracy of 0.948. The area under the receiver operator characteristic curve (AUC) was 0.975. Both indicated sufficient model efficacy. The performance of the model was validated in an external test with high accuracy (0.971) and a high AUC (0.957). Conclusions: High myopia had a high incidence in Guangdong Province. Its onset in children and adolescents was well predicted with the random forest algorithm. Efficient use of real-world data can contribute to the prevention and early diagnosis of high myopia.

Objectives: To evaluate myopia development among primary and secondary school students during the coronavirus 2019 (COVID-19) pandemic.Methods: A cross-sectional study was conducted to evaluate the development of myopia among students in Shenzhen, China during the COVID-19 outbreak.Results: The study included 1,472,957 and 1,573,824 students in 2019 and 2020, respectively. The prevalence of myopia was 46.9 and 50.5% in 2019 and 2020, respectively. The prevalence of myopia among students in the former Shenzhen Special Economic Zone (SEZ) was higher than that in areas outside the former Shenzhen SEZ (2019: 47.0 vs. 43.7%; 2020: 50.5 vs. 47.3%). The prevalence of myopia among girls was higher than that among boys (2019: 50.4 vs. 44.0%; 2020: 54.0 vs. 47.6%). The 50th percentile (P₅₀) of spherical equivalent refraction (SER) in the right eye among girls was lower than that in boys. The prevalence of myopia continued to increase as the grade increased, with the greatest annual increase observed in Grades 2–5 (3.4–3.9%). TheP₅₀of SER in the right eye of students decreased as the grade increased.Conclusions: The prevalence of myopia among students increased during the COVID-19 pandemic, especially in primary school Grades 2–5.

Objectives: To explore machine-learning applications in myopia prediction and analyze the influencing factors of myopia.Methods: Stratified cluster random sampling was used to select elementary school students in Shenzhen, China for inclusion in this case-control study. Myopia screening, ocular biological parameter measurements, and questionnaires were conducted. Random forest (RF), decision tree (DT), extreme gradient boosting trees (XGBoost), support vector machine (SVM), and logistic regression (LR) algorithms were used to construct five myopia prediction models using R software (version 4.3.0). These myopia prediction models were used to investigate the relationship between ocular biological parameters, environmental factors, behavioral factors, genetic factors, and myopia.Results: This study included 2,947 elementary school students, with a myopia prevalence rate of 47.2%. All five prediction models had an area under the receiver operating characteristic curve (AUC) above 0.75, with prediction accuracy and precision exceeding 0.70. The AUCs in the testing set were 0.846, 0.837, 0.833, and 0.815 for SVM, LR, RF, and XGBoost, respectively, indicating their superior predictive performance to that of DT (0.791). In the RF model, the five most important variables were axial length, age, sex, maternal myopia, and feeding pattern. LR identified axial length was the most significant risk factor for myopia [odds ratio (OR) =8.203], followed by sex (OR = 2.349), maternal myopia (OR = 1.437), Reading and writing posture (OR = 1.270), infant feeding pattern (OR = 1.207), and age (OR = 1.168); corneal radius (OR = 0.034) and anterior chamber depth (OR = 0.516) served as protective factors.Conclusion: Myopia prediction models based on machine learning demonstrated favorable predictive performance and accurately identified myopia risk factors, and may therefore aid in the implementation of myopia prevention and control measures among high-risk individuals.

To analyze the factors influencing myopia and construct a nomogram to forecast the risk of myopia among school-age children, providing a reference for identifying high-risk groups to aid prevention and control. This case-control study enrolled 3512 students from three primary schools in Shenzhen using random cluster sampling for a questionnaire survey, myopia screening and ocular biometric parameter measurement. Logistic regression was used to analyze the influencing factors of myopia, and a nomogram was constructed to forecast myopia risk. Bootstrap resampling was used to verify the practicability of the nomogram. Older age (odds ratio[OR] = 1.164; 95% confidence interval [CI]: 1.111-1.219), female sex (OR = 2.405; 95% CI: 2.003-2.887), maternal myopia (OR = 1.331; 95% CI: 1.114-1.589), incorrect posture during reading and writing (OR = 1.283; 95% CI: 1.078-1.528) and axial length (OR = 7.708; 95% CI: 6.044-8.288) are risk factors for myopia, whereas an increase in corneal radius (OR = 0.036; 95% CI: 0.025-0.052) is a protective factor against myopia. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.857, and the net benefit was high when the risk threshold of the decision curve analyses (DCA) ranged from 0.20 to 1.00. The measured values were consistent with the prediction. The nomogram was accurate in predicting the risk of myopia among schoolchildren. This study provides a reference for screening high-risk students and for individualized myopia prevention and control.

Background: Artificial Intelligence (AI), has garnered attention in research on attention deficit hyperactivity disorder (ADHD). In the future, AI may have clinical applications in ADHD, particularly in facilitating the objective diagnosis and classification of ADHD. This study aimed to comprehensively analyze the current status and research frontiers of AI applications in ADHD, identifying hotspots and trends to guide future research directions and promote clinical advancements in this field.Methods: Articles in the field of AI applications in ADHD were from the Web of Science Core Collection (WoSCC) database. Analysis was conducted using CiteSpace 6.3.R.1. Additionally, high-impact articles were analyzed.Results: A total of 342 articles from 50 countries and regions were included. The United States led with 103 articles, having the highest H-index of 21, followed by China with 69 articles, and England with 34 articles. The State University of New York System produced the most articles (11), and Frontiers in Psychiatry had the most articles (12). Burst keywords in 2022–2024 included “diagnosis,” “network,” “attention deficit hyperactivity disorder” and “artificial intelligence.”Conclusion: AI technologies have become a prominent topic in ADHD research, with the United States, China, and England leading in articles and influence. The State University of New York System was the most influential institution, while Frontiers in Psychiatry stood out as the key journal. Utilizing networks and other AI technologies for diagnosing ADHD represents current hotspots and future trends, potentially offering objective indicators for ADHD.