AIMC Topic: Visual Acuity

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Development and Application of an Intelligent Diagnosis System for Retinal Vein Occlusion Based on Deep Learning.

Disease markers
This study is aimed at developing an intelligent algorithm based on deep learning and discussing its application for the classification and diagnosis of retinal vein occlusions (RVO) using fundus images. A total of 501 fundus images of healthy eyes a...

Deep learning to distinguish Best vitelliform macular dystrophy (BVMD) from adult-onset vitelliform macular degeneration (AVMD).

Scientific reports
Initial stages of Best vitelliform macular dystrophy (BVMD) and adult vitelliform macular dystrophy (AVMD) harbor similar blue autofluorescence (BAF) and optical coherence tomography (OCT) features. Nevertheless, BVMD is characterized by a worse fina...

Clinically applicable deep learning-based decision aids for treatment of neovascular AMD.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: Anti-vascular endothelial growth factor (Anti-VEGF) therapy is currently seen as the standard for treatment of neovascular AMD (nAMD). However, while treatments are highly effective, decisions for initial treatment and retreatment are often ...

Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique.

TheScientificWorldJournal
INTRODUCTION: Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence...

Prediction of postoperative visual acuity after vitrectomy for macular hole using deep learning-based artificial intelligence.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To create a model for prediction of postoperative visual acuity (VA) after vitrectomy for macular hole (MH) treatment using preoperative optical coherence tomography (OCT) images, using deep learning (DL)-based artificial intelligence.

Refractive outcomes of second-eye adjustment methods on intraocular lens power calculation in second eye.

Clinical & experimental ophthalmology
BACKGROUND: To investigate the refractive outcomes of second-eye adjustment (SEA) methods in different intraocular lens (IOL) power calculation formulas for second eye following bilateral sequential cataract surgery.

Imaging and artificial intelligence for progression of age-related macular degeneration.

Experimental biology and medicine (Maywood, N.J.)
Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which m...

Development and validation of a deep learning system to classify aetiology and predict anatomical outcomes of macular hole.

The British journal of ophthalmology
AIMS: To develop a deep learning (DL) model for automatic classification of macular hole (MH) aetiology (idiopathic or secondary), and a multimodal deep fusion network (MDFN) model for reliable prediction of MH status (closed or open) at 1 month afte...

AI-based monitoring of retinal fluid in disease activity and under therapy.

Progress in retinal and eye research
Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical exam...