An Open-Source Deep Learning Network for Reconstruction of High-Resolution OCT Angiograms of Retinal Intermediate and Deep Capillary Plexuses

Gao, Min; Hormel, Tristan T.; Wang, Jie; Guo, Yukun; Bailey, Steven T.; Hwang, Thomas S.; Jia, Yali

doi:10.1167/tvst.10.13.13

Cited by 16 publications

(10 citation statements)

References 55 publications

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“…AI methods ( 61 ) have been applied for projection artifact removal ( 60 , 62 ). AI has also been applied to enhance the retinal capillaries ( 63 , 64 ), segment retinal vessels ( 65 , 66 ), quantify the avascular zone ( 67 – 71 ), and map arteries and veins ( 72 ) in OCTA. There are potential opportunities for side-by-side development in humans with the intent to reverse translate.…”

Section: Areas Of Research Opportunities For Reverse Translation Of A...mentioning

confidence: 99%

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Pasquale

Girard

et al. 2023

Front. Ophthalmol.

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Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

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Section: Areas Of Research Opportunities For Reverse Translation Of A...mentioning

confidence: 99%

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Pasquale

Girard

et al. 2023

Front. Ophthalmol.

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“…Data-driven artificial intelligence (AI) methods have offered promising solutions to generative modeling tasks such as denoising of OCT images 20 , 21 , high-resolution reconstruction of OCT angiograms 22 , 23 , and data augmentation in AO images 24 . Here, we explore the potential of AI for recovering the complete cellular structure from only a single noisy AO-OCT acquisition.…”

Section: Introductionmentioning

confidence: 99%

Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography

Das,

Zhang,

Bower

et al. 2024

Commun Med

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Background In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput. Methods Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study. Results We show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges. Conclusions The results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting.

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“…Recent studies in deep learning OCTA have primarily been focused on the classification of eye diseases such as diabetic retinopathy 16 – 18 , age-related macular degeneration 19 – 21 , and glaucoma 22 – 24 . Other applications include improving the image quality of OCTA 25 , 26 and artery–vein segmentation 27 – 31 . Recently, deep learning has also been explored for OCTA construction 32 – 35 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based optical coherence tomography angiography image construction using spatial vascular connectivity network

Le,

Son,

Kim

et al. 2024

Commun Eng

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Optical coherence tomography angiography (OCTA) provides unrivaled capability for depth-resolved visualization of retinal vasculature at the microcapillary level resolution. For OCTA image construction, repeated OCT scans from one location are required to identify blood vessels with active blood flow. The requirement for multi-scan-volumetric OCT can reduce OCTA imaging speed, which will induce eye movements and limit the image field-of-view. In principle, the blood flow should also affect the reflectance brightness profile along the vessel direction in a single-scan-volumetric OCT. Here we report a spatial vascular connectivity network (SVC-Net) for deep learning OCTA construction from single-scan-volumetric OCT. We quantitatively determine the optimal number of neighboring B-scans as image input, we compare the effects of neighboring B-scans to single B-scan input models, and we explore different loss functions for optimization of SVC-Net. This approach can improve the clinical implementation of OCTA by improving transverse image resolution or increasing the field-of-view.

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An Open-Source Deep Learning Network for Reconstruction of High-Resolution OCT Angiograms of Retinal Intermediate and Deep Capillary Plexuses

Cited by 16 publications

References 55 publications

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography

Deep learning-based optical coherence tomography angiography image construction using spatial vascular connectivity network

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