Accurate prediction of myopic progression and high myopia by machine learning

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

Accurate prediction of myopic progression and high myopia by machine learning

Precis Clin Med. 2024 Mar 4;7(1):pbae005. doi: 10.1093/pcmedi/pbae005. eCollection 2024 Mar.

Authors

Jiahui Li^{1

2}, Simiao Zeng^{1

2}, Zhihuan Li³, Jie Xu⁴, Zhuo Sun², Jing Zhao⁵, Meiyan Li⁵, Zixing Zou², Taihua Guan², Jin Zeng², Zhuang Liu³, Wenchao Xiao³, Ran Wei³, Hanpei Miao^{2

6}, Ian Ziyar³, Junxiong Huang³, Yuanxu Gao³, Yangfa Zeng⁷, Xing-Tao Zhou⁵, Kang Zhang^{1

2

3

8}

Affiliations

¹ Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510120, China.
² Guangzhou National Laboratory, Guangzhou 510000, China.
³ Zhuhai International Eye Center of the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Zhuhai 519000, China.
⁴ Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100069, China.
⁵ Key Laboratory of Myopia, Ministry of Health, Shanghai Key Laboratory of Visual Impairment and Restoration, Department of Ophthalmology, Eye Institute, Eye and Ear, Nose, and Throat Hospital, Fudan University, Shanghai 200031, China.
⁶ DongguanPeople's Hospital, The First School of Clinical Medicinel, Southern Medical University, Dongguan 523059, China.
⁷ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.
⁸ Wenzhou Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou 325000, China.

Abstract

Background: 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.

Methods: 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.

Result: The model to predict the progression of myopia achieved an R² value of 0.964 vs a mean absolute error (MAE) of 0.119D [95% confidence interval (CI): 0.119, 1.146] in the internal validation set. It demonstrated strong generalizability, maintaining consistent performance across external validation sets: R² = 0.950 vs MAE = 0.119D (95% CI: 0.119, 1.136) in validation study 1, R² = 0.950 vs MAE = 0.121D (95% CI: 0.121, 1.144) in validation study 2, and R² = 0.806 vs MAE = -0.066D (95% CI: -0.066, 0.569) in the Shanghai Children Myopia Study. In the Beijing Children Eye Study, the model achieved an R² of 0.749 vs a MAE of 0.178D (95% CI: 0.178, 1.557). The model to predict the risk of high myopia achieved an area under the curve (AUC) of 0.99 in the internal validation set and consistently high area under the curve values of 0.99, 0.99, 0.96 and 0.99 in the respective external validation sets.

Conclusion: 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.

Keywords: machine learning; myopia; precision medicine; prevention; progression.