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Retinal Diseases

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Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
INTRODUCTION: Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that ...

Trainable COSFIRE filters for vessel delineation with application to retinal images.

Medical image analysis
Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute t...

RETINAL IMAGING ANALYSIS PERFORMED BY CHATGPT-4o AND GEMINI ADVANCED: The Turning Point of the Revolution?

Retina (Philadelphia, Pa.)
PURPOSE: To assess the diagnostic capabilities of the most recent chatbots releases, GPT-4o and Gemini Advanced, facing different retinal diseases.

Anomaly Detection in Retinal OCT Images With Deep Learning-Based Knowledge Distillation.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop a robust and general purpose artificial intelligence (AI) system that allows the identification of retinal optical coherence tomography (OCT) volumes with pathomorphological manifestations not present...

An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning.

Translational vision science & technology
PURPOSE: To develop an intelligent grading model for myopic maculopathy based on a long-tail learning framework, using the improved loss function LTBSoftmax. The model addresses the long-tail distribution problem in myopic maculopathy data to provide...

A Deep Learning Network for Accurate Retinal Multidisease Diagnosis Using Multiview Fusion of En Face and B-Scan Images: A Multicenter Study.

Translational vision science & technology
PURPOSE: Accurate diagnosis of retinal disease based on optical coherence tomography (OCT) requires scrutiny of both B-scan and en face images. The aim of this study was to investigate the effectiveness of fusing en face and B-scan images for better ...

A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.

JAMA ophthalmology
IMPORTANCE: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a varie...

[The innovation and challenge of artificial intelligence in the whole process management of fundus disease].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
Artificial intelligence (AI) has demonstrated revolutionary potential and wide-ranging applications in the comprehensive management of fundus diseases, yet it faces challenges in clinical translation, data quality, algorithm interpretability, and cro...

Automated Abnormality Detection in Patient Retinal Function: A Deep Learning-Powered Electroretinogram Analysis System.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The electroretinogram (ERG) is an ophthalmic electrophysiology test designed to objectively measure the electrical response of the photoreceptor cells in the human retina. The analysis of the ERG is highly useful in evaluating various retinal disease...

PERFORMANCE ASSESSMENT OF AN ARTIFICIAL INTELLIGENCE CHATBOT IN CLINICAL VITREORETINAL SCENARIOS.

Retina (Philadelphia, Pa.)
PURPOSE: To determine how often ChatGPT is able to provide accurate and comprehensive information regarding clinical vitreoretinal scenarios. To assess the types of sources ChatGPT primarily uses and to determine whether they are hallucinated.