Please use this identifier to cite or link to this item: 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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