AIMC Topic: Retinal Detachment
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Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.
Journal of translational medicine
Apr 30, 2024
BACKGROUND: Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related ...
Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging.
Acta ophthalmologica
Apr 29, 2024
PURPOSE: To elaborate a deep learning (DL) model for automatic prediction of late recurrence (LR) of rhegmatogenous retinal detachment (RRD) using pseudocolor and fundus autofluorescence (AF) ultra-wide field (UWF) images obtained preoperatively and ...
A Deep Learning Model for Detecting Rhegmatogenous Retinal Detachment Using Ophthalmologic Ultrasound Images.
Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde
Dec 19, 2023
INTRODUCTION: Rhegmatogenous retinal detachment (RRD) is one of the most common fundus diseases. Many rural areas of China have few ophthalmologists, and ophthalmologic ultrasound examination is of great significance for remote diagnosis of RRD. Ther...
Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.
Eye (London, England)
Sep 5, 2023
PURPOSE: To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).
Artificial intelligence using deep learning to predict the anatomical outcome of rhegmatogenous retinal detachment surgery: a pilot study.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Oct 28, 2022
PURPOSE: To develop and evaluate an automated deep learning model to predict the anatomical outcome of rhegmatogenous retinal detachment (RRD) surgery.
Deep learning for ultra-widefield imaging: a scoping review.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Jul 20, 2022
PURPOSE: This article is a scoping review of published and peer-reviewed articles using deep-learning (DL) applied to ultra-widefield (UWF) imaging. This study provides an overview of the published uses of DL and UWF imaging for the detection of opht...
Therapeutic response in the HAWK and HARRIER trials using deep learning in retinal fluid volume and compartment analysis.
Eye (London, England)
May 6, 2022
OBJECTIVES: To assess the therapeutic response to brolucizumab and aflibercept by deep learning/OCT-based analysis of macular fluid volumes in neovascular age-related macular degeneration.
Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.
PloS one
Feb 14, 2022
PURPOSE: To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source o...
Subfoveal choroidal thickness changes after intravitreal ranibizumab injections in different patterns of diabetic macular edema using a deep learning-based auto-segmentation.
International ophthalmology
Mar 30, 2021
PURPOSE: To evaluate the effect of intravitreal injection of ranibizumab (IVR) on subfoveal choroidal thickness (SFCT) and its relationship with central macular thickness (CMT) and best-corrected visual acuity (BCVA) changes in eyes with center-invol...
Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Feb 4, 2021
PURPOSE: To investigate the detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus imaging system (Optos) with convolutional neural network technology.
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