Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images
Background: The choroid is a vital vascular layer of the eye, essential for maintaining ocular health. Understanding its structural variations, particularly choroidal thickness (CT), is crucial for the early detection of diseases, such as age-related macular degeneration (AMD), high myopia (HM), and...
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Elsevier
2025-04-01
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| Series: | Photodiagnosis and Photodynamic Therapy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1572100025000419 |
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| author | Dan Song Guanzheng Wang Guangfeng Liu Chengxia Zhang Bin Lv Yuan Ni Guotong Xie |
| author_facet | Dan Song Guanzheng Wang Guangfeng Liu Chengxia Zhang Bin Lv Yuan Ni Guotong Xie |
| author_sort | Dan Song |
| collection | DOAJ |
| description | Background: The choroid is a vital vascular layer of the eye, essential for maintaining ocular health. Understanding its structural variations, particularly choroidal thickness (CT), is crucial for the early detection of diseases, such as age-related macular degeneration (AMD), high myopia (HM), and diabetes mellitus (DM). Recent advancements in deep learning have significantly improved the segmentation and measurement of choroidal layers. Objective: This study aims to investigate age- and gender-related changes in CT and its components through deep learning analysis of swept-source optical coherence tomography (SS-OCT) images. Methods: A total of 262 participants (136 females and 126 males) were recruited from Peking University International Hospital. Exclusion criteria included ocular pathologies and systemic conditions. SS-OCT was utilized for CT, Sattler layer-choriocapillaris complex thickness (SLCCT), and Haller layer thickness (HLT) measurements. auto-measurement method, based on deep learning algorithms, ensured accuracy. Ethics approval and informed consent were obtained from all participants. Findings: Significant thinning of CT and SLCCT was observed after the age of 60, with HLT declining after the age of 30. Females exhibited marked thinning between the ages of 40 and 50, while males began to show thinning at age 60. Conclusion and Implications: This research highlights age-related changes in choroidal thickness, with a particular emphasis on gender differences. The findings suggest that females experience earlier thinning, potentially attributable to hormonal changes. Additionally, the study validates the efficiency of deep learning algorithms in measuring choroidal thickness, thereby enhancing the reliability of clinical practice. |
| format | Article |
| id | doaj-art-54965038573d46d39d034ecd7f12b0ec |
| institution | DOAJ |
| issn | 1572-1000 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photodiagnosis and Photodynamic Therapy |
| spelling | doaj-art-54965038573d46d39d034ecd7f12b0ec2025-08-20T03:00:34ZengElsevierPhotodiagnosis and Photodynamic Therapy1572-10002025-04-015210451110.1016/j.pdpdt.2025.104511Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT imagesDan Song0Guanzheng Wang1Guangfeng Liu2Chengxia Zhang3Bin Lv4Yuan Ni5Guotong Xie6Department of Ophthalmology, Peking University International Hospital, No.1 Shengmingyuan Road, Zhongguancun Life Science Park, Changping District, Beijing, PR ChinaPing An Technology, 12F Building B PingAn IFC No. 1-3 Xinyuan South Road, Beijing, 100027, PR ChinaDepartment of Ophthalmology, Peking University International Hospital, No.1 Shengmingyuan Road, Zhongguancun Life Science Park, Changping District, Beijing, PR China; Corresponding authors.Department of Ophthalmology, Peking University International Hospital, No.1 Shengmingyuan Road, Zhongguancun Life Science Park, Changping District, Beijing, PR ChinaPing An Technology, 12F Building B PingAn IFC No. 1-3 Xinyuan South Road, Beijing, 100027, PR ChinaPing An Technology, 12F Building B PingAn IFC No. 1-3 Xinyuan South Road, Beijing, 100027, PR ChinaPing An Technology, 12F Building B PingAn IFC No. 1-3 Xinyuan South Road, Beijing, 100027, PR China; Ping An Health Cloud Company Limited, 12F Building B, PingAn IFC, No. 1-3 Xinyuan South Road, Beijing, 100027, PR China; Corresponding authors.Background: The choroid is a vital vascular layer of the eye, essential for maintaining ocular health. Understanding its structural variations, particularly choroidal thickness (CT), is crucial for the early detection of diseases, such as age-related macular degeneration (AMD), high myopia (HM), and diabetes mellitus (DM). Recent advancements in deep learning have significantly improved the segmentation and measurement of choroidal layers. Objective: This study aims to investigate age- and gender-related changes in CT and its components through deep learning analysis of swept-source optical coherence tomography (SS-OCT) images. Methods: A total of 262 participants (136 females and 126 males) were recruited from Peking University International Hospital. Exclusion criteria included ocular pathologies and systemic conditions. SS-OCT was utilized for CT, Sattler layer-choriocapillaris complex thickness (SLCCT), and Haller layer thickness (HLT) measurements. auto-measurement method, based on deep learning algorithms, ensured accuracy. Ethics approval and informed consent were obtained from all participants. Findings: Significant thinning of CT and SLCCT was observed after the age of 60, with HLT declining after the age of 30. Females exhibited marked thinning between the ages of 40 and 50, while males began to show thinning at age 60. Conclusion and Implications: This research highlights age-related changes in choroidal thickness, with a particular emphasis on gender differences. The findings suggest that females experience earlier thinning, potentially attributable to hormonal changes. Additionally, the study validates the efficiency of deep learning algorithms in measuring choroidal thickness, thereby enhancing the reliability of clinical practice.http://www.sciencedirect.com/science/article/pii/S1572100025000419Choroidal thicknessHaller layer thicknessSattler layer-choriocapillaris complex thicknessDeep learning algorithm |
| spellingShingle | Dan Song Guanzheng Wang Guangfeng Liu Chengxia Zhang Bin Lv Yuan Ni Guotong Xie Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images Photodiagnosis and Photodynamic Therapy Choroidal thickness Haller layer thickness Sattler layer-choriocapillaris complex thickness Deep learning algorithm |
| title | Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images |
| title_full | Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images |
| title_fullStr | Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images |
| title_full_unstemmed | Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images |
| title_short | Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images |
| title_sort | age and gender related changes in choroidal thickness insights from deep learning analysis of swept source oct images |
| topic | Choroidal thickness Haller layer thickness Sattler layer-choriocapillaris complex thickness Deep learning algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1572100025000419 |
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