Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications
BackgroundAs a disease with high global incidence, hypertension is known to cause systemic vasculopathy. Ophthalmic vessels are the only vascular structures that can be directly observed in vivo in a non-invasive manner. We aim to investigate the changes in ocular microvessels in hypertension using...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Cell and Developmental Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2025.1581785/full |
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| author | Yiheng Ding Ziqiang Wei Chaoyun Wang Xinyue Li Bingbing Li Xueting Liu Zhijie Fu Hongwei Mo Hong Zhang |
| author_facet | Yiheng Ding Ziqiang Wei Chaoyun Wang Xinyue Li Bingbing Li Xueting Liu Zhijie Fu Hongwei Mo Hong Zhang |
| author_sort | Yiheng Ding |
| collection | DOAJ |
| description | BackgroundAs a disease with high global incidence, hypertension is known to cause systemic vasculopathy. Ophthalmic vessels are the only vascular structures that can be directly observed in vivo in a non-invasive manner. We aim to investigate the changes in ocular microvessels in hypertension using deep learning on optical coherence tomography angiography (OCTA) images.MethodsThe convolutional neural network architecture Xception and multi-Swin transformer were used to screen 422 OCTA images (252 from 136 hypertension subjects; 170 from 85 healthy subjects) for hypertension. Moreover, the separability of the OCTA images based on high-dimensional feature angles was analyzed to better understand how deep learning models distinguish such images with class activation mapping.ResultsUnder Xception, the overall average accuracy of 5-fold cross-validation was 76.05% and sensitivity was 85.52%. In contrast, the Swin transformer showed single-model (macular), single-model (optic disk), and multimodel average accuracies of 82.25%, 74.936%, and 85.06%, respectively, for predicting hypertension.ConclusionThe changes caused by hypertension on the fundus vessels can be observed more accurately and efficiently using OCTA image features through deep learning. These results are expected to assist with screening of hypertension and reducing the risk of its severe complications.Trial RegistrationChiCTR, ChiCTR2000041330. Registered 23 December 2020, https://www.chictr.org.cn/ChiCTR2000041330. |
| format | Article |
| id | doaj-art-60f8c9e4f13247d396f5fbcdc52a7c2b |
| institution | Kabale University |
| issn | 2296-634X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj-art-60f8c9e4f13247d396f5fbcdc52a7c2b2025-08-20T03:50:01ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2025-07-011310.3389/fcell.2025.15817851581785Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complicationsYiheng Ding0Ziqiang Wei1Chaoyun Wang2Xinyue Li3Bingbing Li4Xueting Liu5Zhijie Fu6Hongwei Mo7Hong Zhang8Eye Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaSchool of Intelligent Science and Engineering, Harbin Engineering University, Harbin, ChinaSchool of Intelligent Science and Engineering, Harbin Engineering University, Harbin, ChinaEye Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaSchool of Intelligent Science and Engineering, Harbin Engineering University, Harbin, ChinaEye Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaSchool of Intelligent Science and Engineering, Harbin Engineering University, Harbin, ChinaSchool of Intelligent Science and Engineering, Harbin Engineering University, Harbin, ChinaEye Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaBackgroundAs a disease with high global incidence, hypertension is known to cause systemic vasculopathy. Ophthalmic vessels are the only vascular structures that can be directly observed in vivo in a non-invasive manner. We aim to investigate the changes in ocular microvessels in hypertension using deep learning on optical coherence tomography angiography (OCTA) images.MethodsThe convolutional neural network architecture Xception and multi-Swin transformer were used to screen 422 OCTA images (252 from 136 hypertension subjects; 170 from 85 healthy subjects) for hypertension. Moreover, the separability of the OCTA images based on high-dimensional feature angles was analyzed to better understand how deep learning models distinguish such images with class activation mapping.ResultsUnder Xception, the overall average accuracy of 5-fold cross-validation was 76.05% and sensitivity was 85.52%. In contrast, the Swin transformer showed single-model (macular), single-model (optic disk), and multimodel average accuracies of 82.25%, 74.936%, and 85.06%, respectively, for predicting hypertension.ConclusionThe changes caused by hypertension on the fundus vessels can be observed more accurately and efficiently using OCTA image features through deep learning. These results are expected to assist with screening of hypertension and reducing the risk of its severe complications.Trial RegistrationChiCTR, ChiCTR2000041330. Registered 23 December 2020, https://www.chictr.org.cn/ChiCTR2000041330.https://www.frontiersin.org/articles/10.3389/fcell.2025.1581785/fulloptical coherence tomography angiographyhypertensiondeep learningconvolutional neural networkmulti-Swin transformer |
| spellingShingle | Yiheng Ding Ziqiang Wei Chaoyun Wang Xinyue Li Bingbing Li Xueting Liu Zhijie Fu Hongwei Mo Hong Zhang Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications Frontiers in Cell and Developmental Biology optical coherence tomography angiography hypertension deep learning convolutional neural network multi-Swin transformer |
| title | Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications |
| title_full | Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications |
| title_fullStr | Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications |
| title_full_unstemmed | Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications |
| title_short | Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications |
| title_sort | using deep learning to screen octa images for hypertension to reduce the risk of serious complications |
| topic | optical coherence tomography angiography hypertension deep learning convolutional neural network multi-Swin transformer |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2025.1581785/full |
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