1. Articles from An Ran Ran

    1-5 of 5
    1. Three-Dimensional Multi-Task Deep Learning Model to Detect Glaucomatous Optic Neuropathy and Myopic Features From Optical Coherence Tomography Scans: A Retrospective Multi-Centre Study

      Three-Dimensional Multi-Task Deep Learning Model to Detect Glaucomatous Optic Neuropathy and Myopic Features From Optical Coherence Tomography Scans: A Retrospective Multi-Centre Study

      Purpose: We aim to develop a multi-task three-dimensional (3D) deep learning (DL) model to detect glaucomatous optic neuropathy (GON) and myopic features (MF) simultaneously from spectral-domain optical coherence tomography (SDOCT) volumetric scans. Methods: Each volumetric scan was labelled as GON according to the criteria of retinal nerve fibre layer (RNFL) thinning, with a structural defect that correlated in position with the visual field defect (i.e., reference standard). MF were graded by the SDOCT en face images, defined as presence of peripapillary atrophy (PPA), optic disc tilting, or fundus tessellation. The multi-task DL model was developed by ResNet with output ...

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    2. Clinically relevant factors associated with a binary outcome of diabetic macular ischaemia: an OCTA study

      Clinically relevant factors associated with a binary outcome of diabetic macular ischaemia: an OCTA study

      Aims We investigated the demographic, ocular, diabetes-related and systemic factors associated with a binary outcome of diabetic macular ischaemia (DMI) as assessed by optical coherence tomography angiography (OCTA) evaluation of non-perfusion at the level of the superficial capillary plexus (SCP) and deep capillary plexus (DCP) in a cohort of patients with diabetes mellitus (DM). Materials and methods 617 patients with DM were recruited from July 2015 to December 2020 at the Chinese University of Hong Kong Eye Centre. Image quality assessment (gradable or ungradable for assessing DMI) and DMI evaluation (presence or absence of DMI) were assessed at the level ...

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    3. Deep learning in glaucoma with optical coherence tomography: a review

      Deep learning in glaucoma with optical coherence tomography: a review

      Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, applying DL for glaucoma assessment with optical coherence tomography (OCT), including OCT traditional reports, two-dimensional (2D) B-scans, and three-dimensional (3D) volumetric scans, has increasingly raised research interests. Studies have demonstrated that using DL for interpreting OCT is efficient, accurate, and with good performance for discriminating glaucomatous eyes from normal eyes, suggesting that incorporation of DL technology in OCT for glaucoma assessment could potentially address some gaps in the current practice ...

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    4. A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans

      A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans

      Purpose : The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance. Methods : There were 2805 Cirrus optical coherence tomography (OCT) macula volumes (Macula protocol 512 × 128) of 1095 eyes from 586 patients at a single site that were used to train a fully 3D convolutional neural network (CNN). Referable glaucoma included true glaucoma, pre-perimetric glaucoma, and high-risk suspects, based on qualitative fundus photographs, visual fields, OCT reports ...

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    5. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis

      Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis

      Background Spectral-domain optical coherence tomography (SDOCT) can be used to detect glaucomatous optic neuropathy, but human expertise in interpretation of SDOCT is limited. We aimed to develop and validate a three-dimensional (3D) deep-learning system using SDOCT volumes to detect glaucomatous optic neuropathy. Methods We retrospectively collected a dataset including 4877 SDOCT volumes of optic disc cube for training (60%), testing (20%), and primary validation (20%) from electronic medical and research records at the Chinese University of Hong Kong Eye Centre (Hong Kong, China) and the Hong Kong Eye Hospital (Hong Kong, China). Residual network was used to build the 3D ...

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    1-5 of 5
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    Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans Deep learning in glaucoma with optical coherence tomography: a review Clinically relevant factors associated with a binary outcome of diabetic macular ischaemia: an OCTA study Three-Dimensional Multi-Task Deep Learning Model to Detect Glaucomatous Optic Neuropathy and Myopic Features From Optical Coherence Tomography Scans: A Retrospective Multi-Centre Study The truth about invisible posterior vitreous structures Increased Macrophage-like Cell Density in Retinal Vein Occlusion as Characterized by en Face Optical Coherence Tomography The Influence of Eyelid Position and Environmental Conditions on the Corneal Changes in Early Postmortem Interval: A Prospective, Multicentric OCT Study The Use of Optical Coherence Tomography for Gross Examination and Sampling of Fixed Breast Specimens: A Pilot Study The Role of Widefield and Ultra Widefield Optical Coherence Tomography in the Diagnosis and Management of Vitreoretinal Diseases Acute Idiopathic Blind Spot Enlargement Syndrome-New Perspectives in the OCT Era Longitudinal Comparison of Constant Artifacts in Optical Coherence Tomography Angiography in Patients with Posterior Uveitis Compared to Healthy Subjects