A Deep Learning Network for Classifying Arteries and Veins in Montaged Widefield OCT Angiograms

Gao, Min; Guo, Yukun; Hormel, Tristan T.; Tsuboi, Koji; Pacheco, George; Poole, David; Bailey, Steven T.; Flaxel, Christina J.; Huang, David; Hwang, Thomas S.; Jia, Yali

doi:10.1016/j.xops.2022.100149

Cited by 22 publications

(18 citation statements)

References 44 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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“…There is also growing interest in the use of ophthalmic adaptive optics (AO) for retinal vascular biomarkers [125]. A significant association of the retinal vascular structure (inner and outer diameter, parietal thickness) with hypertension has been found [126,127].…”

Section: Ai and Other Retinal Imaging Parametersmentioning

confidence: 99%

Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review

et al. 2022

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The healthcare burden of cardiovascular diseases remains a major issue worldwide. Understanding the underlying mechanisms and improving identification of people with a higher risk profile of systemic vascular disease through noninvasive examinations is crucial. In ophthalmology, retinal vascular network imaging is simple and noninvasive and can provide in vivo information of the microstructure and vascular health. For more than 10 years, different research teams have been working on developing software to enable automatic analysis of the retinal vascular network from different imaging techniques (retinal fundus photographs, OCT angiography, adaptive optics, etc.) and to provide a description of the geometric characteristics of its arterial and venous components. Thus, the structure of

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“…Using a convolutional neural network (CNN), Alam et al 23 demonstrated deep learning AV segmentation with early fusion of en face OCT image and OCTA images for the first time. Using montaged wide-field OCTA images, Gao et al 24 developed a unimodal strategy in deep learning for AV segmentation. Using different approaches, all above mentioned studies exploring the AV classification or segmentation have been primarily focused on the detection and segmentation of large vessels as arteries or veins.…”

Section: Introductionmentioning

confidence: 99%

An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography

Abtahi

Ebrahimi

et al. 2023

Commun Med

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Background Differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) holds promise for the early detection of eye diseases. However, currently available methods for AV analysis are limited for binary processing of retinal vasculature in OCTA, without quantitative information of vascular perfusion intensity. This study is to develop and validate a method for quantitative AV analysis of vascular perfusion intensity. Method A deep learning network AVA-Net has been developed for automated AV area (AVA) segmentation in OCTA. Seven new OCTA features, including arterial area (AA), venous area (VA), AVA ratio (AVAR), total perfusion intensity density (T-PID), arterial PID (A-PID), venous PID (V-PID), and arterial-venous PID ratio (AV-PIDR), were extracted and tested for early detection of diabetic retinopathy (DR). Each of these seven features was evaluated for quantitative evaluation of OCTA images from healthy controls, diabetic patients without DR (NoDR), and mild DR. Results It was observed that the area features, i.e., AA, VA and AVAR, can reveal significant differences between the control and mild DR. Vascular perfusion parameters, including T-PID and A-PID, can differentiate mild DR from control group. AV-PIDR can disclose significant differences among all three groups, i.e., control, NoDR, and mild DR. According to Bonferroni correction, the combination of A-PID and AV-PIDR can reveal significant differences in all three groups. Conclusions AVA-Net, which is available on GitHub for open access, enables quantitative AV analysis of AV area and vascular perfusion intensity. Comparative analysis revealed AV-PIDR as the most sensitive feature for OCTA detection of early DR. Ensemble AV feature analysis, e.g., the combination of A-PID and AV-PIDR, can further improve the performance for early DR assessment.

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A Deep Learning Network for Classifying Arteries and Veins in Montaged Widefield OCT Angiograms

Cited by 22 publications

References 44 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

Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review

An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography

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