1. Articles from David Le

    1-6 of 6
    1. Quantitative optical coherence tomography angiography: A review

      Quantitative optical coherence tomography angiography: A review

      As a new optical coherence tomography (OCT) modality, OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. By providing unparalleled capability to differentiate individual plexus layers in the retina, OCTA has demonstrated its excellence in clinical management of diabetic retinopathy, glaucoma, sickle cell retinopathy, diabetic macular edema, and other eye diseases. Quantitative OCTA analysis of retinal and choroidal vasculatures is essential to standardize objective interpretations of clinical outcome. Quantitative features, including blood vessel tortuosity, blood vessel caliber, blood vessel density, vessel perimeter index, fovea avascular zone area, fovea avascular zone contour irregularity, vessel branching ...

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      Mentions: Xincheng Yao
    2. Longitudinal OCT and OCTA monitoring reveals accelerated regression of hyaloid vessels in retinal degeneration 10 (rd10) mice

      Longitudinal OCT and OCTA monitoring reveals accelerated regression of hyaloid vessels in retinal degeneration 10 (rd10) mice

      The hyaloid vascular system (HVS) is known to have an important role in eye development. However, physiological mechanisms of HVS regression and their correlation with developmental eye disorders remain unclear due to technical limitations of conventional ending point examination with fixed tissues. Here, we report comparative optical coherence tomography (OCT) and OCT angiography (OCTA) monitoring of HVS regression in wild-type and retinal degeneration 10 (rd10) mice. Longitudinal OCTA monitoring revealed accelerated regression of hyaloid vessels correlated with retinal degeneration in rd10. Quantitative OCT measurement disclosed significant distortions of both retinal thickness and the vitreous chamber in rd10 compared to WT ...

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      Mentions: Xincheng Yao
    3. Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy

      Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy

      Purpose: To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy (DR). Methods: A deep learning convolutional neural network (CNN) architecture VGG16 was employed for this study. A transfer learning process was implemented to re-train the CNN for robust OCTA classification. In order to demonstrate the feasibility of using this method for artificial intelligence (AI) screening of DR in clinical environments, the re-trained CNN was incorporated into a custom developed GUI platform which can be readily operated by ophthalmic personnel. Results: With last nine layers re-trained, CNN architecture achieved the best performance ...

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      Mentions: Xincheng Yao
    4. Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies

      Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies

      Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought 1) to differentiate normal from diseased ocular conditions, 2) to ...

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      Mentions: Xincheng Yao
    5. Fully automated geometric feature analysis in optical coherence tomography angiography for objective classification of diabetic retinopathy

      Fully automated geometric feature analysis in optical coherence tomography angiography for objective classification of diabetic retinopathy

      This study is to establish quantitative features of vascular geometry in optical coherence tomography angiography (OCTA) and validate them for the objective classification of diabetic retinopathy (DR). Six geometric features, including total vessel branching angle (VBA: θ), child branching angles (CBAs: α1 and α2), vessel branching coefficient (VBC), and children-to-parent vessel width ratios (VWR1 and VWR2), were automatically derived from each vessel branch in OCTA. Comparative analysis of heathy control, diabetes with no DR (NoDR), and non-proliferative DR (NPDR) was conducted. Our study reveals four quantitative OCTA features to produce robust DR detection and staging classification: (ANOVA, P<0.05), VBA ...

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      Mentions: Xincheng Yao
    6. Supervised machine learning based multi-task artificial intelligence classification of retinopathies

      Supervised machine learning based multi-task artificial intelligence classification of retinopathies

      Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought 1) to differentiate normal from diseased ocular conditions, 2) to ...

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      Mentions: Xincheng Yao
    1-6 of 6
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    1. (6 articles) Xincheng Yao
    2. (1 articles) University of Pennsylvania
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