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

Guan, Jieying; Zhu, Yingting; Hu, Qiuyue; Ma, Shuyue; Mu, Jingfeng; Li, Zhidong; Dong, Fangyuan; Zhuo, Xiaohua; Guan, Haifei; Sun, Qianhui; An, Lin; Zhang, Shaochong; Qin, Peiwu; Zhuo, Yehong

doi:10.2196/39507

Cited by 5 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the models was evaluated using accuracy, precision, recall, F1-score values, and the area under the receiver operating characteristic (ROC) curve (AUC) [ 36 ]. The model that demonstrated the best performance during the validation phase was selected for further analysis.…”

Section: Methodsmentioning

confidence: 99%

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

Xu,

Li,

Han

et al. 2024

Ophthalmol Ther

Self Cite

View full text Add to dashboard Cite

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 ( R 2 = 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. Supplementary Information The online version contains supplementary material available at 10.1007/s40123-024-00972-5.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Xu,

Li,

Han

et al. 2024

Ophthalmol Ther

Self Cite

View full text Add to dashboard Cite

show abstract

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

Ng Yin Ling,

Zhu,

Ang

2024

Current Opinion in Ophthalmology

View full text Add to dashboard Cite

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.

show abstract

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

Li,

Tu,

Zhou

et al. 2024

BMC Ophthalmol

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 5 publications

References 24 publications

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

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

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

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

Contact Info

Product

Resources

About