Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach
Abstract The purpose of this study was to introduce a new machine learning approach for differentiation of a pachychoroid from a healthy choroid based on enhanced depth-optical coherence tomography (EDI-OCT) imaging. This study included EDI-OCT images of 103 eyes from 82 patients with central serous...
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Nature Portfolio
2022-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-20749-9 |
| _version_ | 1797998611159580672 |
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| author | Reza Mirshahi Masood Naseripour Ahmad Shojaei Mohsen Heirani Sayyed Amirpooya Alemzadeh Farzan Moodi Pasha Anvari Khalil Ghasemi Falavarjani |
| author_facet | Reza Mirshahi Masood Naseripour Ahmad Shojaei Mohsen Heirani Sayyed Amirpooya Alemzadeh Farzan Moodi Pasha Anvari Khalil Ghasemi Falavarjani |
| author_sort | Reza Mirshahi |
| collection | DOAJ |
| description | Abstract The purpose of this study was to introduce a new machine learning approach for differentiation of a pachychoroid from a healthy choroid based on enhanced depth-optical coherence tomography (EDI-OCT) imaging. This study included EDI-OCT images of 103 eyes from 82 patients with central serous chorioretinopathy or pachychoroid pigment epitheliopathy, and 103 eyes from 103 age- and sex-matched healthy subjects. Choroidal features including choroidal thickness (CT), choroidal area (CA), Haller layer thickness (HT), Sattler-choriocapillaris thickness (SCT), and the choroidal vascular index (CVI) were extracted. The Haller ratio (HR) was obtained by dividing HT by CT. Multivariate TwoStep cluster analysis was performed with a preset number of two clusters based on a combination of different choroidal features. Clinical criteria were developed based on the results of the cluster analysis, and two independent skilled retina specialists graded a separate testing dataset based on the new clinical criteria. TwoStep cluster analysis achieved a sensitivity of 1.000 (95-CI: 0.938–1.000) and a specificity of 0.986 (95-CI: 0.919–1.000) in the differentiation of pachy- and healthy choroid. The best result for identification of pachychoroid was obtained for a combination of CT, HR, and CVI, with a correct classification rate of 0.993 (95-CI: 0.980–1.000). Based on the relative variable importance (RVI), the cluster analysis prioritized the choroidal features as follows: HR (RVI: 1.0), CVI (RVI: 0.87), CT (RVI: 0.70), CA (RVI: 0.59), and SCT (RVI: 0.27). After performing a receiver operating characteristic curve analysis on the cluster membership variable, a cutoff point of 389 µm and 0.79 was determined for CT and HR, respectively. Based on these clinical criteria, a sensitivity of 0.793 (95-CI: 0.611–0.904) and a specificity of 0.786 (95-CI: 0.600–0.900) and 0.821 (95-CI: 0.638–0.924) were achieved for each grader. Cohen's kappa of inter-rater reliability was 0.895. Based on an unsupervised machine learning approach, a combination of the Haller ratio and choroidal thickness is the most valuable factor in the differentiation of pachy- and healthy choroids in a clinical setting. |
| first_indexed | 2024-04-11T10:51:28Z |
| format | Article |
| id | doaj.art-c79f8100834c4d51a4a63cc1fdb679af |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T10:51:28Z |
| publishDate | 2022-09-01 |
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| spelling | doaj.art-c79f8100834c4d51a4a63cc1fdb679af2022-12-22T04:28:55ZengNature PortfolioScientific Reports2045-23222022-09-011211910.1038/s41598-022-20749-9Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approachReza Mirshahi0Masood Naseripour1Ahmad Shojaei2Mohsen Heirani3Sayyed Amirpooya Alemzadeh4Farzan Moodi5Pasha Anvari6Khalil Ghasemi Falavarjani7Eye Research Center, The Five Senses Institute, Rassoul Akram Hospital, Iran University of Medical SciencesEye Research Center, The Five Senses Institute, Rassoul Akram Hospital, Iran University of Medical SciencesBasir Eye Health Research CenterTranslational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical SciencesEye Research Center, The Five Senses Institute, Rassoul Akram Hospital, Iran University of Medical SciencesEye Research Center, The Five Senses Institute, Rassoul Akram Hospital, Iran University of Medical SciencesEye Research Center, The Five Senses Institute, Rassoul Akram Hospital, Iran University of Medical SciencesEye Research Center, The Five Senses Institute, Rassoul Akram Hospital, Iran University of Medical SciencesAbstract The purpose of this study was to introduce a new machine learning approach for differentiation of a pachychoroid from a healthy choroid based on enhanced depth-optical coherence tomography (EDI-OCT) imaging. This study included EDI-OCT images of 103 eyes from 82 patients with central serous chorioretinopathy or pachychoroid pigment epitheliopathy, and 103 eyes from 103 age- and sex-matched healthy subjects. Choroidal features including choroidal thickness (CT), choroidal area (CA), Haller layer thickness (HT), Sattler-choriocapillaris thickness (SCT), and the choroidal vascular index (CVI) were extracted. The Haller ratio (HR) was obtained by dividing HT by CT. Multivariate TwoStep cluster analysis was performed with a preset number of two clusters based on a combination of different choroidal features. Clinical criteria were developed based on the results of the cluster analysis, and two independent skilled retina specialists graded a separate testing dataset based on the new clinical criteria. TwoStep cluster analysis achieved a sensitivity of 1.000 (95-CI: 0.938–1.000) and a specificity of 0.986 (95-CI: 0.919–1.000) in the differentiation of pachy- and healthy choroid. The best result for identification of pachychoroid was obtained for a combination of CT, HR, and CVI, with a correct classification rate of 0.993 (95-CI: 0.980–1.000). Based on the relative variable importance (RVI), the cluster analysis prioritized the choroidal features as follows: HR (RVI: 1.0), CVI (RVI: 0.87), CT (RVI: 0.70), CA (RVI: 0.59), and SCT (RVI: 0.27). After performing a receiver operating characteristic curve analysis on the cluster membership variable, a cutoff point of 389 µm and 0.79 was determined for CT and HR, respectively. Based on these clinical criteria, a sensitivity of 0.793 (95-CI: 0.611–0.904) and a specificity of 0.786 (95-CI: 0.600–0.900) and 0.821 (95-CI: 0.638–0.924) were achieved for each grader. Cohen's kappa of inter-rater reliability was 0.895. Based on an unsupervised machine learning approach, a combination of the Haller ratio and choroidal thickness is the most valuable factor in the differentiation of pachy- and healthy choroids in a clinical setting.https://doi.org/10.1038/s41598-022-20749-9 |
| spellingShingle | Reza Mirshahi Masood Naseripour Ahmad Shojaei Mohsen Heirani Sayyed Amirpooya Alemzadeh Farzan Moodi Pasha Anvari Khalil Ghasemi Falavarjani Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach Scientific Reports |
| title | Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach |
| title_full | Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach |
| title_fullStr | Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach |
| title_full_unstemmed | Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach |
| title_short | Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach |
| title_sort | differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach |
| url | https://doi.org/10.1038/s41598-022-20749-9 |
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