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https://hdl.handle.net/10356/179616
Title: | Optical coherence tomography choroidal enhancement using generative deep learning | Authors: | Bellemo, Valentina Das, Ankit Kumar Sreng, Syna Chua, Jacqueline Wong, Damon Shah, Janika Jonas, Rahul Tan, Bingyao Liu, Xinyu Xu, Xinxing Tan, Gavin Siew Wei Agrawal, Rupesh Ting, Daniel Shu Wei Yong, Liu Schmetterer, Leopold |
Keywords: | Medicine, Health and Life Sciences | Issue Date: | 2024 | Source: | Bellemo, V., Das, A. K., Sreng, S., Chua, J., Wong, D., Shah, J., Jonas, R., Tan, B., Liu, X., Xu, X., Tan, G. S. W., Agrawal, R., Ting, D. S. W., Yong, L. & Schmetterer, L. (2024). Optical coherence tomography choroidal enhancement using generative deep learning. Npj Digital Medicine, 7(1). https://dx.doi.org/10.1038/s41746-024-01119-3 | Project: | CG/C010A/2017_SERI OFLCG/004c/2018-00 MOH-000249-00 MOH-000647-00 MOH-001001-00 MOH-001015-00 MOH-000500-00 MOH-000707-00 MOH-001072-06 MOH-001286-00 NRF2019-THE002-0006 NRF-CRP24-2020- 0001 A20H4b0141 LF1019-1 |
Journal: | npj Digital Medicine | Abstract: | Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts’ ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson’s correlations of 0.97 [95% CI: 0.96–0.98], 0.97 [0.95–0.98], 0.95 [0.92–0.98], and 0.87 [0.83–0.91], with intra-class correlation values of 0.99 [0.98–0.99], 0.98 [0.98–0.99], and 0.95 [0.96–0.98], 0.93 [0.91–0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions. | URI: | https://hdl.handle.net/10356/179616 | ISSN: | 2398-6352 | DOI: | 10.1038/s41746-024-01119-3 | Schools: | Lee Kong Chian School of Medicine (LKCMedicine) School of Chemical and Biomedical Engineering School of Chemistry, Chemical Engineering and Biotechnology |
Organisations: | National Eye Centre SERI-NTU Advanced Ocular Engineering (STANCE) Program Duke-NUS Medical School Tan Tock Seng Hospital |
Rights: | © 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/. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | LKCMedicine Journal Articles |
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