Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients
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 subject...
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Frontiers Media S.A.
2021-04-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2021.657566/full |
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| author | 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 |
| author_facet | 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 |
| author_sort | Dandan Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T09:08:40Z |
| format | Article |
| id | doaj.art-4217df85b7ec409a908aaf872e79a492 |
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| language | English |
| last_indexed | 2024-12-14T09:08:40Z |
| publishDate | 2021-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj.art-4217df85b7ec409a908aaf872e79a4922022-12-21T23:08:37ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2021-04-01810.3389/fmed.2021.657566657566Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic PatientsDandan Sun0Dandan Sun1Dandan Sun2Dandan Sun3Dandan Sun4Dandan Sun5Yuchen Du6Qiuying Chen7Qiuying Chen8Qiuying Chen9Qiuying Chen10Qiuying Chen11Qiuying Chen12Luyao Ye13Luyao Ye14Luyao Ye15Luyao Ye16Luyao Ye17Luyao Ye18Huai Chen19Menghan Li20Menghan Li21Menghan Li22Menghan Li23Menghan Li24Menghan Li25Jiangnan He26Jiangnan He27Jiangnan He28Jiangnan He29Jiangnan He30Jiangnan He31Jianfeng Zhu32Jianfeng Zhu33Jianfeng Zhu34Jianfeng Zhu35Jianfeng Zhu36Jianfeng Zhu37Lisheng Wang38Ying Fan39Ying Fan40Ying Fan41Ying Fan42Ying Fan43Ying Fan44Xun Xu45Xun Xu46Xun Xu47Xun Xu48Xun Xu49Xun Xu50Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaNational Clinical Research Center for Eye Diseases, Shanghai, ChinaShanghai Key Laboratory of Ocular Fundus Disease, Shanghai, ChinaShanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, ChinaShanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, ChinaDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, ChinaDepartment of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaNational Clinical Research Center for Eye Diseases, Shanghai, ChinaShanghai Key Laboratory of Ocular Fundus Disease, Shanghai, ChinaShanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, ChinaShanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, ChinaDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaNational Clinical Research Center for Eye Diseases, Shanghai, ChinaShanghai Key Laboratory of Ocular Fundus Disease, Shanghai, ChinaShanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, ChinaShanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, ChinaDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, ChinaDepartment of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaNational Clinical Research Center for Eye Diseases, Shanghai, ChinaShanghai Key Laboratory of Ocular Fundus Disease, Shanghai, ChinaShanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, ChinaShanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, ChinaDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaNational Clinical Research Center for Eye Diseases, Shanghai, ChinaShanghai Key Laboratory of Ocular Fundus Disease, Shanghai, ChinaShanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, ChinaShanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, ChinaDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaNational Clinical Research Center for Eye Diseases, Shanghai, ChinaShanghai Key Laboratory of Ocular Fundus Disease, Shanghai, ChinaShanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, ChinaShanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, ChinaDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, ChinaDepartment of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaNational Clinical Research Center for Eye Diseases, Shanghai, ChinaShanghai Key Laboratory of Ocular Fundus Disease, Shanghai, ChinaShanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, ChinaShanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, ChinaDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaNational Clinical Research Center for Eye Diseases, Shanghai, ChinaShanghai Key Laboratory of Ocular Fundus Disease, Shanghai, ChinaShanghai Engineering Center for Visual Science and Photo Medicine, Shanghai, ChinaShanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, ChinaDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, ChinaPurpose: 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.https://www.frontiersin.org/articles/10.3389/fmed.2021.657566/fullmyopiamachine learningradiomicsoptic discchoroidal thickness |
| spellingShingle | 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 Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients Frontiers in Medicine myopia machine learning radiomics optic disc choroidal thickness |
| title | Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients |
| title_full | Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients |
| title_fullStr | Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients |
| title_full_unstemmed | Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients |
| title_short | Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients |
| title_sort | imaging features by machine learning for quantification of optic disc changes and impact on choroidal thickness in young myopic patients |
| topic | myopia machine learning radiomics optic disc choroidal thickness |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2021.657566/full |
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