1. Articles from David Le

    1-4 of 4
    1. 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
    2. 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
    3. 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
    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 ...

      Read Full Article
      Mentions: Xincheng Yao
    1-4 of 4
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    1. (4 articles) Xincheng Yao
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    Supervised machine learning based multi-task artificial intelligence classification of retinopathies Fully automated geometric feature analysis in optical coherence tomography angiography for objective classification of diabetic retinopathy Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy Intravitreal Ranibizumab Monotherapy or Combined with Laser for Diabetic Macular Edema (OCT guided study) Prospective evaluation of drug eluting self‐apposing stent for the treatment of unprotected left main coronary artery disease: 1‐year results of the TRUNC study Clinical validation of the RTVue optical coherence tomography angiography image quality indicators Intraoperative OCT-Assisted Retinal Detachment Repair in the DISCOVER Study: Impact and Outcomes Analysis of Retinal Vascular Density using Optical Coherence Tomography Angiography, to Differentiate Healthy, Glaucoma Suspect and Glaucomatous Eyes (Thesis) Correlation between in vivo near-infrared spectroscopy and optical coherence tomography detected lipid-rich plaques with post-mortem histology Simultaneous morphological and flow imaging enabled by megahertz intravascular Doppler optical coherence tomography Ultrastructural analysis of a corneal dellen using optical coherence tomography