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Lin D, Chen J, Lin Z, Li X, Zhang K, Wu X, Liu Z, Huang J, Li J, Zhu Y, Chen C, Zhao L, Xiang Y, Guo C, Wang L, Liu Y, Chen W, Lin H. A practical model for the identification of congenital cataracts using machine learning. EBioMedicine 2020; 51:102621. [PMID: 31901869 PMCID: PMC6948173 DOI: 10.1016/j.ebiom.2019.102621] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 12/18/2019] [Accepted: 12/19/2019] [Indexed: 12/29/2022] Open
Abstract
Background Approximately 1 in 33 newborns is affected by congenital anomalies worldwide. We aimed to develop a practical model for identifying infants with a high risk of congenital cataracts (CCs), which is the leading cause of avoidable childhood blindness. Methods This case-control study was performed in the Zhongshan Ophthalmic Center and involved 2005 subjects, including 1274 children with CCs and 731 healthy controls. The CC identification models were established based on birth conditions, family medical history, and family environmental factors using the random forest (RF) and adaptive boosting methods (trained by 1129 CC cases and 609 healthy controls), which were tested by internal 4-fold cross-validation and external validation (145 CC cases and 122 healthy controls). The models were also tested using 4 datasets with gradually reduced proportions of CC patients (bilateral cases) to validate their performance in an approximate simulation of a clinical environment with a relatively low disease prevalence. Findings The CC identification models showed high discrimination in both the 4-fold cross validation (area under the curve (AUC)=0.91 [95% confidence interval: 0.88–0.94] in bilateral cases; 0.82 [0.77–0.89] in unilateral cases) and external validation (AUC=0.93±0.05 in bilateral cases; 0.86±0.01 in unilateral cases), and achieved stable performance in the clinical tests (AUC=0.94–0.96 in the four subgroups by RF). Furthermore, family history of CC, low parental education level, and comorbidity were identified as the top three most relevant factors to both bilateral and unilateral CC diagnosis. Interpretation Our CC identification models can accurately discriminate CC patients from healthy children and have the potential to serve as a complementary screening procedure, especially in undeveloped and remote areas.
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Affiliation(s)
- Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Jingjing Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Zhuoling Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Xiaoyan Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Kai Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China; School of Computer Science and Technology, Xidian University, Xi'an, Shanxi 710071, People's Republic of China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Zhenzhen Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Jialing Huang
- School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510060, People's Republic of China
| | - Jing Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Yi Zhu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China; Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Chuan Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China; Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Yifan Xiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Chong Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Liming Wang
- School of Computer Science and Technology, Xidian University, Xi'an, Shanxi 710071, People's Republic of China
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China
| | - Weirong Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China.
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Jinsui Road #7, Guangzhou, Guangdong 510060, People's Republic of China.
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