A machine-learning approach to discerning prevalence and causes of myopia among elementary students in Hubei

Bibliometric analysis of hotspots and trends of global myopia research

AIM

To gain insights into the global research hotspots and trends of myopia.

METHODS

Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software.

RESULTS

A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers.

CONCLUSION

Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

Ciliary muscles contraction leads to axial length extension--The possible initiating factor for myopia

PURPOSE

This study aimed to investigate the underlying factors driving the onset of myopia, specifically the role of the ciliary muscle's contraction in the elongation of the axial length of the eye.

METHODS

The retrospective study was conducted utilizing data from three ophthalmic centers in Shanghai and Beijing. Both Chinese and Caucasian children were involved. The axial length of the subjects' eyes was measured in both relaxed and contracted state of the ciliary muscle. A comprehensive mechanical model was also developed to observe the influence of ciliary muscle contraction on the axial length.

RESULTS

This study included a sample of 198 right eyes of 198 myopic children. Of these, 97 were male and 101 were female, 126 were of Chinese ethnicity and 72 were Caucasian. The age of onset for myopia ranged from 5.9 to 16.9 years old. The axial length of the eye decreased 0.028 ± 0.007mm following dilation, indicating relaxation of the ciliary muscle (t paired student = 15.16, p = 6.72 x 10-35). In contrast, ciliary muscle contraction resulted in an increase in axial length. Considering proportionality, a significant 90.4% (179 eyes) exhibited a reduced axial length, while a minor 9.6% (19 eyes) demonstrated an increase post-mydriasis. Finite element modeling demonstrated that muscle contraction caused a tension force that transmits towards the posterior pole of the eye, causing it to extend posteriorly.

CONCLUSION

The contraction of the ciliary muscle leads to an extension of the axial length. This could potentially be the initiating factor for myopia.

24-H movement behaviors and visual impairment among Chinese adolescents with and without obesity

BACKGROUND

Previous studies have identified obesity, sleep patterns, screen time, and physical activity as independent risk factors for the visual health of adolescents. However, our understanding of how these factors interact and contribute to visual impairment remains limited. This study aimed to investigate the relationship between adherence to the 24-h movement guidelines (24-HMG) and visual impairment in adolescents with and without obesity.

METHODS

We analyzed data from the 2014-2015 China Education Panel Survey. Participants provided self-reported information on their screen time, sleep duration, and physical activity levels. The data on weight, height, and visual acuity were obtained from school health examination reports. Logistic regression analysis was conducted to assess the association between 24-h movement behaviors and visual impairment, reported as odds ratios (ORs) with a 95 % confidence interval (CI).

RESULTS

After controlling for covariates such as sex and age, it was found that adolescents with obesity who adhered to the sleep guidelines had a lower risk of visual impairment compared with adolescents without obesity who did not adhere to the 24-HMG (OR = 0.84, 95 % CI: 0.75-0.94, P = 0.003). Additionally, adolescents who adhered to both the physical activity and sleep guidelines had an even lower risk of visual impairment (OR = 0.58, 95 % CI: 0.42-0.79, P = 0.001).

CONCLUSIONS

Adhering to the Sleep and physical activity + Sleep recommendations in the 24-HMG could significantly reduce the risk of visual impairment in adolescents without obesity. No significant relationship was observed between adherence to 24-HMG and the risk of visual impairment in adolescents with obesity.

Machine learning to analyze the factors influencing myopia in students of different school periods

Purpose

We aim to develop myopia classification models based on machine learning algorithms for each schooling period, and further analyze the similarities and differences in the factors influencing myopia in each school period based on each model.

Design

Retrospective cross-sectional study.

Participants

We collected visual acuity, behavioral, environmental, and genetic data from 7,472 students in 21 primary and secondary schools (grades 1-12) in Jiamusi, Heilongjiang Province, using visual acuity screening and questionnaires.

Methods

Machine learning algorithms were used to construct myopia classification models for students at the whole schooling period, primary school, junior high school, and senior high school period, and to rank the importance of features in each model.

Results

The main influencing factors for students differ by school section, The optimal machine learning model for the whole schooling period was Random Forest (AUC = 0.752), with the top three influencing factors being age, myopic grade of the mother, and Whether myopia requires glasses. The optimal model for the primary school period was a Random Forest (AUC = 0.710), with the top three influences being the myopic grade of the mother, age, and extracurricular tutorials weekly. The Junior high school period was an Support Vector Machine (SVM; AUC = 0.672), and the top three influencing factors were gender, extracurricular tutorial subjects weekly, and whether can you do the "three ones" when reading and writing. The senior high school period was an XGboost (AUC = 0.722), and the top three influencing factors were the need for spectacles for myopia, average daily time spent outdoors, and the myopic grade of the mother.

Conclusion

Factors such as genetics and eye use behavior all play an essential role in students' myopia, but there are differences between school periods, with those in the lower levels focusing on genetics and those in the higher levels focusing on behavior, but both play an essential role in myopia.