Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort.

Introduction: An important obstacle in the fight against diabetic retinopathy (DR) is the use of a classification system based on old imaging techniques and insufficient data to accurately predict its evolution. New imaging techniques generate new valuable data, but we lack an adapted classification based on these data. The main objective of the Evaluation Intelligente de la Rétinopathie Diabétique, Intelligent evaluation of DR (EviRed) project is to develop and validate a system assisting the ophthalmologist in decision-making during DR follow-up by improving the prediction of its evolution.

Hybrid Fusion of High-Resolution and Ultra-Widefield OCTA Acquisitions for the Automatic Diagnosis of Diabetic Retinopathy.

Optical coherence tomography angiography (OCTA) can deliver enhanced diagnosis for diabetic retinopathy (DR). This study evaluated a deep learning (DL) algorithm for automatic DR severity assessment using high-resolution and ultra-widefield (UWF) OCTA. Diabetic patients were examined with 6×6 mm2 high-resolution OCTA and 15×15 mm2 UWF-OCTA using PLEX®Elite 9000.

Reliability and Reproducibility of Pigment Epithelial Detachment Volume Measurements in AMD Using a New Tool: ReVAnalyzer.

Background And Objective: To describe and present the reliability and reproducibility of a new software, Retinal Volume Analyzer (ReVAnalyzer), for pigment epithelium detachment (PED) volume quantification.

Patients And Methods: This is a retrospective study including patients with PEDs secondary to exudative age-related macular degeneration (AMD). Macular volume scans on spectral-domain optical coherence tomography on enhanced depth imaging mode were performed in all eyes.

Exudate detection in color retinal images for mass screening of diabetic retinopathy.

The automatic detection of exudates in color eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to automatically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced.

Multimedia data mining for automatic diabetic retinopathy screening.

This paper presents TeleOphta, an automatic system for screening diabetic retinopathy in teleophthalmology networks. Its goal is to reduce the burden on ophthalmologists by automatically detecting non referable examination records, i.e.

Studying disagreements among retinal experts through image analysis.

In recent years, many image analysis algorithms have been presented to assist Diabetic Retinopathy (DR) screening. The goal was usually to detect healthy examination records automatically, in order to reduce the number of records that should be analyzed by retinal experts. In this paper, a novel application is presented: these algorithms are used to 1) discover image characteristics that sometimes cause an expert to disagree with his/her peers and 2) warn the expert whenever these characteristics are detected in an examination record.

Rapid image evaluation system for corneal in vivo confocal microscopy.

Purpose: To develop rapid image processing techniques for the objective analysis of corneal in vivo confocal micrographs.

Methods: Perpendicular central corneal volume scans from healthy volunteers were obtained via laser in vivo confocal microscopy. The layer in each volume scan that contained the nerve plexus was detected by applying software operators to analyze image features on the basis of their size, shape, and contrast.

A multiple-instance learning framework for diabetic retinopathy screening.

A novel multiple-instance learning framework, for automated image classification, is presented in this paper. Given reference images marked by clinicians as relevant or irrelevant, the image classifier is trained to detect patterns, of arbitrary size, that only appear in relevant images. After training, similar patterns are sought in new images in order to classify them as either relevant or irrelevant images.