Jiahui Li, Simiao Zeng, Zhihuan Li, Jie Xu, Zhuo Sun, Jing Zhao, Meiyan Li, Zixing Zou, Taihua Guan, Jin Zeng, Zhuang Liu, Wenchao Xiao, Ran Wei, Hanpei Miao, Ian Ziyar, Junxiong Huang, Yuanxu Gao, Yangfa Zeng, Xing-Tao Zhou, Kang Zhang
{"title":"Accurate prediction of myopic progression and high myopia by machine learning","authors":"Jiahui Li, Simiao Zeng, Zhihuan Li, Jie Xu, Zhuo Sun, Jing Zhao, Meiyan Li, Zixing Zou, Taihua Guan, Jin Zeng, Zhuang Liu, Wenchao Xiao, Ran Wei, Hanpei Miao, Ian Ziyar, Junxiong Huang, Yuanxu Gao, Yangfa Zeng, Xing-Tao Zhou, Kang Zhang","doi":"10.1093/pcmedi/pbae005","DOIUrl":null,"url":null,"abstract":"\n \n \n Myopia is a leading cause of visual impairment in Asia and worldwide. However, accurately predicting the progression of myopia and the high risk of myopia remains a challenge. This study aims to develop a predictive model for the development of myopia.\n \n \n \n We first retrospectively gathered 612 530 medical records from five independent cohorts, encompassing 227 543 patients ranging from infants to young adults. Subsequently, we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia.\n \n \n \n The model to predict the progression of myopia achieved an R-squared (R2) value of 0.964 vs a mean absolute error (MAE) of 0.119D (95% CI: 0.119, 1.146) in the internal validation set. It demonstrated strong generalizability, maintaining consistent performance across external validation sets: R2 of 0.950 vs MAE of 0.119D (95% CI: 0.119, 1.136) in validation Study 1, R2 of 0.950 vs MAE of 0.121D (95% CI: 0.121, 1.144) in validation Study 2, and R2 of 0.806 vs MAE of −0.066D (95% CI: −0.066, 0.569) in Shanghai Children Myopia Study. Beijing Children Eye Study, the model sustained R2 of 0.749 vs MAE of 0.178D (95% CI: 0.178, 1.557). The model to predict the risk of high myopia achieved and an area under the curve (AUC) of 0.99 in the internal validation set and consistently high AUC values of 0.99, 0.99,0.96 and 0.99 in the respective external validation sets.\n \n \n \n Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.\n","PeriodicalId":5,"journal":{"name":"ACS Applied Materials & Interfaces","volume":"77 17","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Materials & Interfaces","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/pcmedi/pbae005","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Myopia is a leading cause of visual impairment in Asia and worldwide. However, accurately predicting the progression of myopia and the high risk of myopia remains a challenge. This study aims to develop a predictive model for the development of myopia.
We first retrospectively gathered 612 530 medical records from five independent cohorts, encompassing 227 543 patients ranging from infants to young adults. Subsequently, we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia.
The model to predict the progression of myopia achieved an R-squared (R2) value of 0.964 vs a mean absolute error (MAE) of 0.119D (95% CI: 0.119, 1.146) in the internal validation set. It demonstrated strong generalizability, maintaining consistent performance across external validation sets: R2 of 0.950 vs MAE of 0.119D (95% CI: 0.119, 1.136) in validation Study 1, R2 of 0.950 vs MAE of 0.121D (95% CI: 0.121, 1.144) in validation Study 2, and R2 of 0.806 vs MAE of −0.066D (95% CI: −0.066, 0.569) in Shanghai Children Myopia Study. Beijing Children Eye Study, the model sustained R2 of 0.749 vs MAE of 0.178D (95% CI: 0.178, 1.557). The model to predict the risk of high myopia achieved and an area under the curve (AUC) of 0.99 in the internal validation set and consistently high AUC values of 0.99, 0.99,0.96 and 0.99 in the respective external validation sets.
Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.
期刊介绍:
ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.