1. Articles from R. V. P. Chan

    1-3 of 3
    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. 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
    1-3 of 3
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  2. Topics in the News

    1. (3 articles) Xincheng Yao
    2. (1 articles) University College London
    3. (1 articles) University of North Carolina
    4. (1 articles) Bern University Hospital
    5. (1 articles) Nieves Gonzalo
    6. (1 articles) Fernando Alfonso
    7. (1 articles) Santiago Jiménez-Valero
    8. (1 articles) Amy L. Oldenburg
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