Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults
Background: Myopia affects 1.4 billion individuals worldwide. Notably, there is increasing evidence that choroidal thickness plays an important role in myopia and risk of developing myopia-related conditions. With the advancements in artificial intelligence (AI), choroidal thickness segmentation can...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/179693 |
| _version_ | 1826126201838632960 |
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| author | Lim, Zhi Wei Li, Jonathan Wong, Damon Chung, Joey Toh, Angeline Lee, Jia Ling Lam, Crystal Balakrishnan, Maithily Chia, Audrey Chua, Jacqueline Girard, Michael Hoang, Quan V. Chong, Rachel Wong, Chee Wai Saw, Seang Mei Schmetterer, Leopold Brennan, Noel Ang, Marcus |
| author2 | School of Chemistry, Chemical Engineering and Biotechnology |
| author_facet | School of Chemistry, Chemical Engineering and Biotechnology Lim, Zhi Wei Li, Jonathan Wong, Damon Chung, Joey Toh, Angeline Lee, Jia Ling Lam, Crystal Balakrishnan, Maithily Chia, Audrey Chua, Jacqueline Girard, Michael Hoang, Quan V. Chong, Rachel Wong, Chee Wai Saw, Seang Mei Schmetterer, Leopold Brennan, Noel Ang, Marcus |
| author_sort | Lim, Zhi Wei |
| collection | NTU |
| description | Background: Myopia affects 1.4 billion individuals worldwide. Notably, there is increasing evidence that choroidal thickness plays an important role in myopia and risk of developing myopia-related conditions. With the advancements in artificial intelligence (AI), choroidal thickness segmentation can now be automated, offering inherent advantages such as better repeatability, reduced grader variability, and less reliance for manpower. Hence, we aimed to evaluate the agreement between AI-automated and manual segmented measurements of subfoveal choroidal thickness (SFCT) using two swept-source optical coherence tomography (OCT) systems. Methods: Subjects aged ≥ 16 years, with myopia of ≥ 0.50 diopters in both eyes, were recruited from the Prospective Myopia Cohort Study in Singapore (PROMYSE). OCT scans were acquired using Triton DRI-OCT and PLEX Elite 9000. OCT images were segmented both automatically with an established SA-Net architecture and manually using a standard technique with adjudication by two independent graders. SFCT was subsequently determined based on the segmentation. The Bland–Altman plot and intraclass correlation coefficient (ICC) were used to evaluate the agreement. Results: A total of 229 subjects (456 eyes) with mean [± standard deviation (SD)] age of 34.1 (10.4) years were included. The overall SFCT (mean ± SD) based on manual segmentation was 216.9 ± 82.7 µm with Triton DRI-OCT and 239.3 ± 84.3 µm with PLEX Elite 9000. ICC values demonstrated excellent agreement between AI-automated and manual segmented SFCT measurements (PLEX Elite 9000: ICC = 0.937, 95% CI: 0.922 to 0.949, P < 0.001; Triton DRI-OCT: ICC = 0.887, 95% CI: 0.608 to 0.950, P < 0.001). For PLEX Elite 9000, manual segmented measurements were generally thicker when compared to AI-automated segmented measurements, with a fixed bias of 6.3 µm (95% CI: 3.8 to 8.9, P < 0.001) and proportional bias of 0.120 (P < 0.001). On the other hand, manual segmented measurements were comparatively thinner than AI-automated segmented measurements for Triton DRI-OCT, with a fixed bias of − 26.7 µm (95% CI: − 29.7 to − 23.7, P < 0.001) and proportional bias of − 0.090 (P < 0.001). Conclusion: We observed an excellent agreement in choroidal segmentation measurements when comparing manual with AI-automated techniques, using images from two SS-OCT systems. Given its edge over manual segmentation, automated segmentation may potentially emerge as the primary method of choroidal thickness measurement in the future. |
| first_indexed | 2024-10-01T06:48:49Z |
| format | Journal Article |
| id | ntu-10356/179693 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:48:49Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1796932024-08-23T15:31:41Z Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults Lim, Zhi Wei Li, Jonathan Wong, Damon Chung, Joey Toh, Angeline Lee, Jia Ling Lam, Crystal Balakrishnan, Maithily Chia, Audrey Chua, Jacqueline Girard, Michael Hoang, Quan V. Chong, Rachel Wong, Chee Wai Saw, Seang Mei Schmetterer, Leopold Brennan, Noel Ang, Marcus School of Chemistry, Chemical Engineering and Biotechnology Singapore Eye Research Institute Singapore National Eye Centre Duke-NUS Medical School SERI-NTU Advanced Ocular Engineering (STANCE) Medicine, Health and Life Sciences Choroidal thickness Myopia Background: Myopia affects 1.4 billion individuals worldwide. Notably, there is increasing evidence that choroidal thickness plays an important role in myopia and risk of developing myopia-related conditions. With the advancements in artificial intelligence (AI), choroidal thickness segmentation can now be automated, offering inherent advantages such as better repeatability, reduced grader variability, and less reliance for manpower. Hence, we aimed to evaluate the agreement between AI-automated and manual segmented measurements of subfoveal choroidal thickness (SFCT) using two swept-source optical coherence tomography (OCT) systems. Methods: Subjects aged ≥ 16 years, with myopia of ≥ 0.50 diopters in both eyes, were recruited from the Prospective Myopia Cohort Study in Singapore (PROMYSE). OCT scans were acquired using Triton DRI-OCT and PLEX Elite 9000. OCT images were segmented both automatically with an established SA-Net architecture and manually using a standard technique with adjudication by two independent graders. SFCT was subsequently determined based on the segmentation. The Bland–Altman plot and intraclass correlation coefficient (ICC) were used to evaluate the agreement. Results: A total of 229 subjects (456 eyes) with mean [± standard deviation (SD)] age of 34.1 (10.4) years were included. The overall SFCT (mean ± SD) based on manual segmentation was 216.9 ± 82.7 µm with Triton DRI-OCT and 239.3 ± 84.3 µm with PLEX Elite 9000. ICC values demonstrated excellent agreement between AI-automated and manual segmented SFCT measurements (PLEX Elite 9000: ICC = 0.937, 95% CI: 0.922 to 0.949, P < 0.001; Triton DRI-OCT: ICC = 0.887, 95% CI: 0.608 to 0.950, P < 0.001). For PLEX Elite 9000, manual segmented measurements were generally thicker when compared to AI-automated segmented measurements, with a fixed bias of 6.3 µm (95% CI: 3.8 to 8.9, P < 0.001) and proportional bias of 0.120 (P < 0.001). On the other hand, manual segmented measurements were comparatively thinner than AI-automated segmented measurements for Triton DRI-OCT, with a fixed bias of − 26.7 µm (95% CI: − 29.7 to − 23.7, P < 0.001) and proportional bias of − 0.090 (P < 0.001). Conclusion: We observed an excellent agreement in choroidal segmentation measurements when comparing manual with AI-automated techniques, using images from two SS-OCT systems. Given its edge over manual segmentation, automated segmentation may potentially emerge as the primary method of choroidal thickness measurement in the future. Published version Singapore Eye Research Institute - Johnson & Johnson Vision Care Joint Research Programme for Myopia, Industry Alignment Fund – Industry Collaboration Project (ICP-1700052). 2024-08-19T00:43:36Z 2024-08-19T00:43:36Z 2024 Journal Article Lim, Z. W., Li, J., Wong, D., Chung, J., Toh, A., Lee, J. L., Lam, C., Balakrishnan, M., Chia, A., Chua, J., Girard, M., Hoang, Q. V., Chong, R., Wong, C. W., Saw, S. M., Schmetterer, L., Brennan, N. & Ang, M. (2024). Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults. Eye and Vision, 11(1), 21-. https://dx.doi.org/10.1186/s40662-024-00385-2 2326-0254 https://hdl.handle.net/10356/179693 10.1186/s40662-024-00385-2 38831465 2-s2.0-85195399041 1 11 21 en ICP-1700052 Eye and Vision © 2024 The Author(s). Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. application/pdf |
| spellingShingle | Medicine, Health and Life Sciences Choroidal thickness Myopia Lim, Zhi Wei Li, Jonathan Wong, Damon Chung, Joey Toh, Angeline Lee, Jia Ling Lam, Crystal Balakrishnan, Maithily Chia, Audrey Chua, Jacqueline Girard, Michael Hoang, Quan V. Chong, Rachel Wong, Chee Wai Saw, Seang Mei Schmetterer, Leopold Brennan, Noel Ang, Marcus Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults |
| title | Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults |
| title_full | Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults |
| title_fullStr | Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults |
| title_full_unstemmed | Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults |
| title_short | Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults |
| title_sort | comparison of manual and artificial intelligence automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults |
| topic | Medicine, Health and Life Sciences Choroidal thickness Myopia |
| url | https://hdl.handle.net/10356/179693 |
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