Heatmap analysis for artificial intelligence explainability in diabetic retinopathy detection: illuminating the rationale of deep learning decisions.

Background: The opaqueness of artificial intelligence (AI) algorithms decision processes limit their application in healthcare. Our objective was to explore discrepancies in heatmaps originated from slightly different retinal images from the same eyes of individuals with diabetes, to gain insights into the deep learning (DL) decision process.

Methods: Pairs of retinal images from the same eyes of individuals with diabetes, composed of images obtained before and after pupil dilation, underwent automatic analysis by a convolutional neural network for the presence of diabetic retinopathy (DR), output being a score ranging from 0 to 1.

BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.

Introduction: The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups.

Automated machine learning model for fundus image classification by health-care professionals with no coding experience.

To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset.

Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol.

Purpose: To evaluate the performance of artificial intelligence (AI) systems embedded in a mobile, handheld retinal camera, with a single retinal image protocol, in detecting both diabetic retinopathy (DR) and more-than-mild diabetic retinopathy (mtmDR).

Design: Multicenter cross-sectional diagnostic study, conducted at 3 diabetes care and eye care facilities.

Participants: A total of 327 individuals with diabetes mellitus (type 1 or type 2) underwent a retinal imaging protocol enabling expert reading and automated analysis.

Transition from an in-person to a telemedicine diabetic retinopathy screening program.

Purpose: Timely screening and treatment are essential for preventing diabetic retinopathy blindness. Improving screening workflows can reduce waiting times for specialist evaluation and thus enhance patient outcomes. This study assessed different screening approaches in a Brazilian public healthcare setting.