Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach
Abstract To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-85189-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850150420885798912 |
|---|---|
| author | Nicola Valsecchi Elham Sadeghi Elli Davis Mohammed Nasar Ibrahim Nasiq Hasan Sandeep Chandra Bollepalli Sumit Randhir Singh Luigi Fontana Jose Alain Sahel Kiran Kumar Vupparaboina Jay Chhablani |
| author_facet | Nicola Valsecchi Elham Sadeghi Elli Davis Mohammed Nasar Ibrahim Nasiq Hasan Sandeep Chandra Bollepalli Sumit Randhir Singh Luigi Fontana Jose Alain Sahel Kiran Kumar Vupparaboina Jay Chhablani |
| author_sort | Nicola Valsecchi |
| collection | DOAJ |
| description | Abstract To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach. Phansalkar thresholding was employed to binarize the choroidal vasculature. The choroidal vessels were visualized in 3D maps, and divided into five sectors: nasal, temporal, superior, inferior, and central. Choroidal thickness (CT) and choroidal vascularity index (CVI) of the whole volumes were calculated using the automated software. The three vessels for each sector were measured, to obtain the mean choroidal vessel diameter (MChVD). The inter-vessel distance (IVD) was defined as the distance between the vessel and the nearest non-collateral vessel. The choroidal biomarkers obtained were compared between different age groups (18 to 34 years old, 35 to 59 years old, and ≥ 60) and sex. Linear mixed models and univariate analysis were used for statistical analysis. A total of 80 eyes of 53 patients were included in the analysis. The mean age of the patients was 44.7 ± 18.5 years, and 54.7% were females. Overall, 44 eyes of 29 females and 36 eyes of 24 males were included in the study. We observed that 33% of the eyes presented at least one choroidal vessel larger than 200 μm crossing the central 3000 μm of the macula. Also, we observed a significant decrease in mean CVI with advancing age (p < 0.05), whereas no significant changes in mean MChVD and IVD were observed (p > 0.05). Furthermore, CVI was increased in females compared to males in each sector, with a significant difference in the temporal sector (p < 0.05). MChVD and IVD did not show any changes with increasing age, whereas CVI decreased with increasing age. Also, CVI was increased in healthy females compared to males. The 3D assessment of choroidal vessels using a deep-learning approach represents an innovative, non-invasive technique for investigating choroidal vasculature, with potential applications in research and clinical practice. |
| format | Article |
| id | doaj-art-22e00976a3ad453cb9ec405e98535bd1 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-22e00976a3ad453cb9ec405e98535bd12025-08-20T02:26:33ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-025-85189-7Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approachNicola Valsecchi0Elham Sadeghi1Elli Davis2Mohammed Nasar Ibrahim3Nasiq Hasan4Sandeep Chandra Bollepalli5Sumit Randhir Singh6Luigi Fontana7Jose Alain Sahel8Kiran Kumar Vupparaboina9Jay Chhablani10Department of Ophthalmology, School of Medicine, University of PittsburghDepartment of Ophthalmology, School of Medicine, University of PittsburghDepartment of Ophthalmology, School of Medicine, University of PittsburghDepartment of Ophthalmology, School of Medicine, University of PittsburghDepartment of Ophthalmology, School of Medicine, University of PittsburghDepartment of Ophthalmology, School of Medicine, University of PittsburghSri Sai Lions Netralaya and Sri Sai Eye HospitalOphthalmology Unit, Dipartimento di Scienze Mediche e Chirurgiche, Alma Mater Studiorum University of BolognaDepartment of Ophthalmology, School of Medicine, University of PittsburghDepartment of Ophthalmology, School of Medicine, University of PittsburghDepartment of Ophthalmology, School of Medicine, University of PittsburghAbstract To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach. Phansalkar thresholding was employed to binarize the choroidal vasculature. The choroidal vessels were visualized in 3D maps, and divided into five sectors: nasal, temporal, superior, inferior, and central. Choroidal thickness (CT) and choroidal vascularity index (CVI) of the whole volumes were calculated using the automated software. The three vessels for each sector were measured, to obtain the mean choroidal vessel diameter (MChVD). The inter-vessel distance (IVD) was defined as the distance between the vessel and the nearest non-collateral vessel. The choroidal biomarkers obtained were compared between different age groups (18 to 34 years old, 35 to 59 years old, and ≥ 60) and sex. Linear mixed models and univariate analysis were used for statistical analysis. A total of 80 eyes of 53 patients were included in the analysis. The mean age of the patients was 44.7 ± 18.5 years, and 54.7% were females. Overall, 44 eyes of 29 females and 36 eyes of 24 males were included in the study. We observed that 33% of the eyes presented at least one choroidal vessel larger than 200 μm crossing the central 3000 μm of the macula. Also, we observed a significant decrease in mean CVI with advancing age (p < 0.05), whereas no significant changes in mean MChVD and IVD were observed (p > 0.05). Furthermore, CVI was increased in females compared to males in each sector, with a significant difference in the temporal sector (p < 0.05). MChVD and IVD did not show any changes with increasing age, whereas CVI decreased with increasing age. Also, CVI was increased in healthy females compared to males. The 3D assessment of choroidal vessels using a deep-learning approach represents an innovative, non-invasive technique for investigating choroidal vasculature, with potential applications in research and clinical practice.https://doi.org/10.1038/s41598-025-85189-7Choroidal vesselsOCTA3DChoroidal vascularity indexHealthy eyes |
| spellingShingle | Nicola Valsecchi Elham Sadeghi Elli Davis Mohammed Nasar Ibrahim Nasiq Hasan Sandeep Chandra Bollepalli Sumit Randhir Singh Luigi Fontana Jose Alain Sahel Kiran Kumar Vupparaboina Jay Chhablani Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach Scientific Reports Choroidal vessels OCTA 3D Choroidal vascularity index Healthy eyes |
| title | Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach |
| title_full | Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach |
| title_fullStr | Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach |
| title_full_unstemmed | Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach |
| title_short | Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach |
| title_sort | assessment of choroidal vessels in healthy eyes using 3 dimensional vascular maps and a semi automated deep learning approach |
| topic | Choroidal vessels OCTA 3D Choroidal vascularity index Healthy eyes |
| url | https://doi.org/10.1038/s41598-025-85189-7 |
| work_keys_str_mv | AT nicolavalsecchi assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT elhamsadeghi assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT ellidavis assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT mohammednasaribrahim assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT nasiqhasan assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT sandeepchandrabollepalli assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT sumitrandhirsingh assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT luigifontana assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT josealainsahel assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT kirankumarvupparaboina assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach AT jaychhablani assessmentofchoroidalvesselsinhealthyeyesusing3dimensionalvascularmapsandasemiautomateddeeplearningapproach |