AIMC Topic: Fluorescein Angiography
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Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.
Scientific reports
Dec 17, 2022
We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss t...
KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD.
Computer methods and programs in biomedicine
Dec 15, 2022
BACKGROUND AND OBJECTIVES: Automated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal Choroidal...
Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma.
Current eye research
Sep 22, 2022
PURPOSE: We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective...
Prediction of the response to photodynamic therapy in patients with chronic central serous chorioretinopathy based on optical coherence tomography using deep learning.
Photodiagnosis and photodynamic therapy
Sep 5, 2022
PURPOSE: To assess the prediction of the response to photodynamic therapy (PDT) in chronic central serous chorioretinopathy (CSCR) based on spectral-domain optical coherence tomography (SD-OCT) images using deep learning (DL).
Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.
BMC ophthalmology
Sep 1, 2022
BACKGROUND: To assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to synthesize clinically useful optical coherence tomography (OCT) color-coded macular thickness maps based on a modest-sized original fluorescein angiograp...
Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multimodal Imaging.
Ophthalmology. Retina
Aug 28, 2022
OBJECTIVE: To develop deep learning models for annualized geographic atrophy (GA) growth rate prediction using fundus autofluorescence (FAF) images and spectral-domain OCT volumes from baseline visits, which can be used for prognostic covariate adjus...
A Deep Learning Model for Automated Segmentation of Geographic Atrophy Imaged Using Swept-Source OCT.
Ophthalmology. Retina
Aug 12, 2022
PURPOSE: To present a deep learning algorithm for segmentation of geographic atrophy (GA) using en face swept-source OCT (SS-OCT) images that is accurate and reproducible for the assessment of GA growth over time.
Deep learning for quality assessment of optical coherence tomography angiography images.
Scientific reports
Aug 12, 2022
Optical coherence tomography angiography (OCTA) is an emerging non-invasive technique for imaging the retinal vasculature. While there are many promising clinical applications for OCTA, determination of image quality remains a challenge. We developed...
Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives.
Progress in retinal and eye research
Aug 4, 2022
When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount...
Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework.
Computational and mathematical methods in medicine
Aug 4, 2022
OBJECTIVE: As an extension of optical coherence tomography (OCT), optical coherence tomographic angiography (OCTA) provides information on the blood flow status at the microlevel and is sensitive to changes in the fundus vessels. However, due to the ...
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