Optical coherence tomography choroidal enhancement using generative deep learning

被引:6
作者
Bellemo, Valentina [1 ,2 ,3 ]
Kumar Das, Ankit [4 ]
Sreng, Syna [1 ,3 ]
Chua, Jacqueline [1 ,3 ,5 ]
Wong, Damon [1 ,3 ,5 ,6 ]
Shah, Janika [1 ,5 ]
Jonas, Rahul [7 ,8 ]
Tan, Bingyao [1 ,3 ,7 ,8 ]
Liu, Xinyu [1 ,3 ,5 ]
Xu, Xinxing [4 ]
Tan, Gavin Siew Wei [1 ,5 ]
Agrawal, Rupesh [1 ,2 ,9 ]
Ting, Daniel Shu Wei [1 ,5 ]
Yong, Liu [1 ,4 ]
Schmetterer, Leopold [1 ,2 ,3 ,5 ,6 ,10 ,11 ,12 ]
机构
[1] Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore
[2] Nanyang Technol Univ, Lee Kong Chian Sch Med, Singapore, Singapore
[3] SERI NTU Adv Ocular Engn STANCE Program, Singapore, Singapore
[4] ASTAR, Inst High Performance Comp, Singapore, Singapore
[5] Duke NUS Med Sch, Ophthalmol & Visual Sci Acad Clin Program Eye ACP, Singapore, Singapore
[6] Med Univ Vienna, Ctr Med Phys & Biomed Engn, Vienna, Austria
[7] Univ Cologne, Fac Med, Cologne, Germany
[8] Univ Hosp Cologne, Dept Ophthalmol, Cologne, Germany
[9] Nanyang Technol Univ NTU, Tan Tock Seng Hosp, Natl Healthcare Grp Eye Inst, Singapore Sch Chem & Biomed Engn, Singapore, Singapore
[10] Nanyang Technol Univ, Sch Chem Chem Engn & Biotechnol, Singapore, Singapore
[11] Med Univ Vienna, Dept Clin Pharmacol, Vienna, Austria
[12] Inst Mol & Clin Ophthalmol, Basel, Switzerland
基金
英国医学研究理事会; 新加坡国家研究基金会;
关键词
DIABETIC-RETINOPATHY; RISK-FACTORS; OCT IMAGES; THICKNESS; PREVALENCE; SEGMENTATION; DEGENERATION; BINARIZATION; FEATURES; EYES;
D O I
10.1038/s41746-024-01119-3
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页数:12
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