AIMC Topic: Fundus Oculi

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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
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...

Quality assessment of colour fundus and fluorescein angiography images using deep learning.

The British journal of ophthalmology
BACKGROUND/AIMS: Image quality assessment (IQA) is crucial for both reading centres in clinical studies and routine practice, as only adequate quality allows clinicians to correctly identify diseases and treat patients accordingly. Here we aim to dev...

Explainable artificial intelligence for the automated assessment of the retinal vascular tortuosity.

Medical & biological engineering & computing
Retinal vascular tortuosity is an excessive bending and twisting of the blood vessels in the retina that is associated with numerous health conditions. We propose a novel methodology for the automated assessment of the retinal vascular tortuosity fro...

Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images.

Medical engineering & physics
The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the Gravitational search optimization ...

Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning.

The British journal of ophthalmology
BACKGROUND/AIMS: Fundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA images is complex and time-consuming, and the ability of diagnosis is uneven a...

Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy.

Scientific reports
To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patie...

Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department.

American journal of ophthalmology
PURPOSE: The Fundus photography vs Ophthalmoscopy Trial Outcomes in the Emergency Department (FOTO-ED) studies showed that ED providers poorly recognized funduscopic findings in patients in the ED. We tested a modified version of the Brain and Optic ...

Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features.

PloS one
The number of diabetic retinopathy (DR) patients is increasing every year, and this causes a public health problem. Therefore, regular diagnosis of diabetes patients is necessary to avoid the progression of DR stages to advanced stages that lead to b...

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study.

International journal of molecular sciences
Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identif...

Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising.

IEEE journal of biomedical and health informatics
Distributed big data and digital healthcare technologies have great potential to promote medical services, but challenges arise when it comes to learning predictive model from diverse and complex e-health datasets. Federated Learning (FL), as a colla...