Machine Learning to Determine Risk Factors for Myopia Progression in Primary School Children: The Anyang Childhood Eye Study

Li, Shiming; Ren, Mingyang; Gan, Jiahe; Zhang, San-Guo; Kang, Meng-Tian; Li, He; Atchison, David A.; Rozema, Jos J; Grzybowski, Andrzej; Wang, Ning Li

doi:10.1007/s40123-021-00450-2

Cited by 22 publications

(15 citation statements)

References 36 publications

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“…A series of protective and risk factors for myopia were screened, and a risk prediction model based on SVM was highly accurate in predicting the occurrence of myopia in the future. Compared to using a single model, Li et al (2022a) introduced the idea of ensemble learning and constructed a strong classifier by integrating a large number of decision trees as the basic unit. However, there was no significant improvement in the results, which may be related to the dataset and the selection of predictor variables.…”

Section: Ai Technology For Myopia Risk Predictionmentioning

confidence: 99%

Artificial intelligence technology for myopia challenges: A review

Zhang

Zou

2023

Front. Cell Dev. Biol.

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Myopia is a significant global health concern and affects human visual function, resulting in blurred vision at a distance. There are still many unsolved challenges in this field that require the help of new technologies. Currently, artificial intelligence (AI) technology is dominating medical image and data analysis and has been introduced to address challenges in the clinical practice of many ocular diseases. AI research in myopia is still in its early stages. Understanding the strengths and limitations of each AI method in specific tasks of myopia could be of great value and might help us to choose appropriate approaches for different tasks. This article reviews and elaborates on the technical details of AI methods applied for myopia risk prediction, screening and diagnosis, pathogenesis, and treatment.

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Section: Ai Technology For Myopia Risk Predictionmentioning

confidence: 99%

Artificial intelligence technology for myopia challenges: A review

Zhang

Zou

2023

Front. Cell Dev. Biol.

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“…In equation (6), D means the distance, C denotes all students, c i is the centroid, n is a certain student, and M is the final score.…”

Section: Construction Of Mhm Based On Bdtmentioning

confidence: 99%

“…The optimization of students' psychological quality and improving their mental health level has become one of the focuses of current education [ 5 ]. Mental health counseling (MHC) for PSE students is needed for the healthy growth of students and the main content of mental health education [ 6 ]. Accordingly, the mental health of PSE students will be studied.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

Liu

2022

Computational Intelligence and Neuroscience

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The purpose was to timely identify the mental disorders (MDs) of students receiving primary and secondary education (PSE) (PSE students) and improve their mental quality. Firstly, this work analyzes the research status of the mental health model (MHM) and the main contents of PSE student-oriented mental health quality cultivation under deep learning (DL). Secondly, an MHM is implemented based on big data technology (BDT) and the convolutional neural network (CNN). Simultaneously, the long short-term memory (LSTM) is introduced to optimize the proposed MHM. Finally, the performance of the MHM before and after optimization is evaluated, and the PSE student-oriented mental health quality training strategy based on the proposed MHM is offered. The results show that the accuracy curve is higher than the recall curve in all classification algorithms. The maximum recall rate is 0.58, and the minimum accuracy rate is 0.62. The decision tree (DT) algorithm has the best comprehensive performance among the five different classification algorithms, with accuracy of 0.68, recall rate of 0.58, and F1-measure of 0.69. Thus, the DT algorithm is selected as the classifier. The proposed MHM can identify 56% of students with MDs before optimization. After optimization, the accuracy is improved by 0.03. The recall rate is improved by 0.19, the F1-measure is improved by 0.05, and 75% of students with MDs can be identified. Diverse behavior data can improve the recognition effect of students’ MDs. Meanwhile, from the 60th iteration, the mode accuracy and loss tend to be stable. By comparison, batch_size has little influence on the experimental results. The number of convolution kernels of the first convolution layer has little influence. The proposed MHM based on DL and CNN will indirectly improve the mental health quality of PSE students. The research provides a reference for cultivating the mental health quality of PSE students.

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“…Artificial intelligence (AI) may help in obtaining useful information from this screening data. Wang et al [8] trained and validated a random forest algorithm to yield uncorrected distance visual acuity (UDVA) and spherical equivalents (SE) that predict myopia progression in children. Lin et al [9] developed an algorithm to predict SE and the onset of high myopia up to 8 years ahead by considering variables including age, SE, and annual progression rate.…”

Section: Introductionmentioning

confidence: 99%

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

Guan¹,

Zhu²,

Hu³

et al. 2023

J Med Internet Res

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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.

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Machine Learning to Determine Risk Factors for Myopia Progression in Primary School Children: The Anyang Childhood Eye Study

Cited by 22 publications

References 36 publications

Artificial intelligence technology for myopia challenges: A review

Artificial intelligence technology for myopia challenges: A review

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

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

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