Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods
Abstract Background Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic d...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-04-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-021-02818-1 |
| _version_ | 1818937471608553472 |
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| author | Yuchen Du Qiuying Chen Ying Fan Jianfeng Zhu Jiangnan He Haidong Zou Dazhen Sun Bowen Xin David Feng Michael Fulham Xiuiyng Wang Lisheng Wang Xun Xu |
| author_facet | Yuchen Du Qiuying Chen Ying Fan Jianfeng Zhu Jiangnan He Haidong Zou Dazhen Sun Bowen Xin David Feng Michael Fulham Xiuiyng Wang Lisheng Wang Xun Xu |
| author_sort | Yuchen Du |
| collection | DOAJ |
| description | Abstract Background Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage. Methods A total of 457 eyes (313 patients) were enrolled and were divided into severe MM group and without severe MM group. Radiomics analysis was applied to depict features significantly correlated with severe MM from optic disc region. Receiver Operating Characteristic were used to evaluate these features’ performance of classifying severe MM. Results Eight new MM-related image features were discovered from the optic disc region, which described the shapes, textural patterns and intensity distributions of optic disc region. Compared with clinically reported MM-related features, these newly discovered features exhibited better abilities on severe MM classification. And the mean values of most features were markedly changed between patients with peripapillary diffuse chorioretinal atrophy (PDCA) and macular diffuse chorioretinal atrophy (MDCA). Conclusions Machine learning and radiomics method are useful tools for mining more MM-related features from the optic disc region, by which complex or even hidden MM-related features can be discovered and decoded. In this paper, eight new MM-related image features were found, which would be useful for further quantitative study of MM-progression. As a nontrivial byproduct, marked changes between PDCA and MDCA was discovered by both new image features and clinic features. |
| first_indexed | 2024-12-20T05:52:29Z |
| format | Article |
| id | doaj.art-be0d5ede2e1048e9a1ed445b4ff70624 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-12-20T05:52:29Z |
| publishDate | 2021-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-be0d5ede2e1048e9a1ed445b4ff706242022-12-21T19:51:08ZengBMCJournal of Translational Medicine1479-58762021-04-0119111210.1186/s12967-021-02818-1Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methodsYuchen Du0Qiuying Chen1Ying Fan2Jianfeng Zhu3Jiangnan He4Haidong Zou5Dazhen Sun6Bowen Xin7David Feng8Michael Fulham9Xiuiyng Wang10Lisheng Wang11Xun Xu12The Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University (SJTU)Department of Preventative Ophthalmology, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye HospitalDepartment of Preventative Ophthalmology, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye HospitalDepartment of Preventative Ophthalmology, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye HospitalDepartment of Preventative Ophthalmology, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye HospitalDepartment of Preventative Ophthalmology, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye HospitalThe Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University (SJTU)Biomedical and Multimedia Information Technology Research Group, School of Computer Science, The University of SydneyBiomedical and Multimedia Information Technology Research Group, School of Computer Science, The University of SydneyDepartment of Molecular Imaging, Royal Prince Alfred Hospital and the University of SydneyBiomedical and Multimedia Information Technology Research Group, School of Computer Science, The University of SydneyThe Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University (SJTU)Department of Preventative Ophthalmology, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye HospitalAbstract Background Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage. Methods A total of 457 eyes (313 patients) were enrolled and were divided into severe MM group and without severe MM group. Radiomics analysis was applied to depict features significantly correlated with severe MM from optic disc region. Receiver Operating Characteristic were used to evaluate these features’ performance of classifying severe MM. Results Eight new MM-related image features were discovered from the optic disc region, which described the shapes, textural patterns and intensity distributions of optic disc region. Compared with clinically reported MM-related features, these newly discovered features exhibited better abilities on severe MM classification. And the mean values of most features were markedly changed between patients with peripapillary diffuse chorioretinal atrophy (PDCA) and macular diffuse chorioretinal atrophy (MDCA). Conclusions Machine learning and radiomics method are useful tools for mining more MM-related features from the optic disc region, by which complex or even hidden MM-related features can be discovered and decoded. In this paper, eight new MM-related image features were found, which would be useful for further quantitative study of MM-progression. As a nontrivial byproduct, marked changes between PDCA and MDCA was discovered by both new image features and clinic features.https://doi.org/10.1186/s12967-021-02818-1Pathologic myopiaMyopic maculopathyFeature miningMachine learningRadiomics |
| spellingShingle | Yuchen Du Qiuying Chen Ying Fan Jianfeng Zhu Jiangnan He Haidong Zou Dazhen Sun Bowen Xin David Feng Michael Fulham Xiuiyng Wang Lisheng Wang Xun Xu Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods Journal of Translational Medicine Pathologic myopia Myopic maculopathy Feature mining Machine learning Radiomics |
| title | Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods |
| title_full | Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods |
| title_fullStr | Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods |
| title_full_unstemmed | Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods |
| title_short | Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods |
| title_sort | automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods |
| topic | Pathologic myopia Myopic maculopathy Feature mining Machine learning Radiomics |
| url | https://doi.org/10.1186/s12967-021-02818-1 |
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