Frontiers in Medicine (Apr 2021)
Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients
- Dandan Sun,
- Dandan Sun,
- Dandan Sun,
- Dandan Sun,
- Dandan Sun,
- Dandan Sun,
- Yuchen Du,
- Qiuying Chen,
- Qiuying Chen,
- Qiuying Chen,
- Qiuying Chen,
- Qiuying Chen,
- Qiuying Chen,
- Luyao Ye,
- Luyao Ye,
- Luyao Ye,
- Luyao Ye,
- Luyao Ye,
- Luyao Ye,
- Huai Chen,
- Menghan Li,
- Menghan Li,
- Menghan Li,
- Menghan Li,
- Menghan Li,
- Menghan Li,
- Jiangnan He,
- Jiangnan He,
- Jiangnan He,
- Jiangnan He,
- Jiangnan He,
- Jiangnan He,
- Jianfeng Zhu,
- Jianfeng Zhu,
- Jianfeng Zhu,
- Jianfeng Zhu,
- Jianfeng Zhu,
- Jianfeng Zhu,
- Lisheng Wang,
- Ying Fan,
- Ying Fan,
- Ying Fan,
- Ying Fan,
- Ying Fan,
- Ying Fan,
- Xun Xu,
- Xun Xu,
- Xun Xu,
- Xun Xu,
- Xun Xu,
- Xun Xu
Affiliations
- Dandan Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Dandan Sun
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Dandan Sun
- Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai, China
- Dandan Sun
- Shanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, China
- Dandan Sun
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Dandan Sun
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China
- Yuchen Du
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
- Qiuying Chen
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Qiuying Chen
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Qiuying Chen
- Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai, China
- Qiuying Chen
- Shanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, China
- Qiuying Chen
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Qiuying Chen
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China
- Luyao Ye
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Luyao Ye
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Luyao Ye
- Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai, China
- Luyao Ye
- Shanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, China
- Luyao Ye
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Luyao Ye
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China
- Huai Chen
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
- Menghan Li
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Menghan Li
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Menghan Li
- Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai, China
- Menghan Li
- Shanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, China
- Menghan Li
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Menghan Li
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China
- Jiangnan He
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Jiangnan He
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Jiangnan He
- Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai, China
- Jiangnan He
- Shanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, China
- Jiangnan He
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Jiangnan He
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China
- Jianfeng Zhu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Jianfeng Zhu
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Jianfeng Zhu
- Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai, China
- Jianfeng Zhu
- Shanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, China
- Jianfeng Zhu
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Jianfeng Zhu
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China
- Lisheng Wang
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
- Ying Fan
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Ying Fan
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Ying Fan
- Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai, China
- Ying Fan
- Shanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, China
- Ying Fan
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Ying Fan
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China
- Xun Xu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Xun Xu
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Xun Xu
- Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai, China
- Xun Xu
- Shanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, China
- Xun Xu
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Xun Xu
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China
- DOI
- https://doi.org/10.3389/fmed.2021.657566
- Journal volume & issue
-
Vol. 8
Abstract
Purpose: To construct quantifiable models of imaging features by machine learning describing early changes of optic disc and peripapillary region, and to explore their performance as early indicators for choroidal thickness (ChT) in young myopic patients.Methods: Eight hundred and ninety six subjects were enrolled. Imaging features were extracted from fundus photographs. Macular ChT (mChT) and peripapillary ChT (pChT) were measured on swept-source optical coherence tomography scans. All participants were divided randomly into training (70%) and test (30%) sets. Imaging features correlated with ChT were selected by LASSO regression and combined into new indicators of optic disc (IODs) for mChT (IOD_mChT) and for pChT (IOD_pChT) by multivariate regression models in the training set. The performance of IODs was evaluated in the test set.Results: A significant correlation between IOD_mChT and mChT (r = 0.650, R2 = 0.423, P < 0.001) was found in the test set. IOD_mChT was negatively associated with axial length (AL) (r = −0.562, P < 0.001) and peripapillary atrophy (PPA) area (r = −0.738, P < 0.001) and positively associated with ovality index (r = 0.503, P < 0.001) and torsion angle (r = 0.242, P < 0.001) in the test set. Every 1 × 10 μm decrease in IOD_mChT was associated with an 8.87 μm decrease in mChT. A significant correlation between IOD_pChT and pChT (r = 0.576, R2 = 0.331, P < 0.001) was found in the test set. IOD_pChT was negatively associated with AL (r = −0.478, P < 0.001) and PPA area (r = −0.651, P < 0.001) and positively associated with ovality index (r = 0.285, P < 0.001) and torsion angle (r = 0.180, P < 0.001) in the test set. Every 1 × 10 μm decrease in IOD_pChT was associated with a 9.64 μm decrease in pChT.Conclusions: The study introduced a machine learning approach to acquire imaging information of early changes of optic disc and peripapillary region and constructed quantitative models significantly correlated with choroidal thickness. The objective models from fundus photographs represented a new approach that offset limitations of human annotation and could be applied in other areas of fundus diseases.
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