1. 1-3 of 3
    1. A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis

      A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis

      OBJECTIVE Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. RESEARCH DESIGN AND METHODS We trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively. For both 3D and 2D CNNs, we used the residual network (ResNet) as the backbone. For the 3D CNN, we ...

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    2. Carotid Disease and Retinal Optical Coherence Tomography Angiography Parameters in Type 2 Diabetes: The Fremantle Diabetes Study Phase II

      Carotid Disease and Retinal Optical Coherence Tomography Angiography Parameters in Type 2 Diabetes: The Fremantle Diabetes Study Phase II

      OBJECTIVE To use optical coherence tomography angiography (OCTA) to determine whether retinal microvascular parameters are associated with carotid arterial disease in people with type 2 diabetes. RESEARCH DESIGN AND METHODS Participants (community-based) underwent detailed assessments including carotid ultrasonography and OCTA. Ultrasound images were assessed for mean intima-media thickness (IMT) and the presence of stenosis. OCTA image analysis provided measures of vessel density, foveal avascular zone (FAZ) area, blood flow areas, and retinal thickness. For each OCTA variable, the most parsimonious model was generated using generalized estimating equations, then ipsilateral and contralateral carotid disease related variables were added to determine their ...

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    3. Incorporating Optical Coherence Tomography Macula Scans Enhances Cost-effectiveness of Fundus Photography-Based Screening for Diabetic Macula Edema

      Incorporating Optical Coherence Tomography Macula Scans Enhances Cost-effectiveness of Fundus Photography-Based Screening for Diabetic Macula Edema

      OBJECTIVE To compare four screening strategies for diabetic macular edema (DME). RESEARCH DESIGN AND METHODS Patients attending diabetic retinopathy screening were recruited and received macular optical coherence tomography (OCT), in addition to visual acuity (VA) and fundus photography (FP) assessments, as part of the standard protocol. Two retina specialists provided the reference grading by independently assessing each subject’s screened data for DME. The current standard protocol (strategy A) was compared for sensitivity, specificity, quality-adjusted life-year (QALY) gained, and incremental cost-effectiveness ratio (ICER) with three alternative candidate protocols using a simulation model with the same subjects. In strategy B, macular ...

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