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.

Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.

Purpose: This scoping review aims to explore the current applications of ChatGPT in the retina field, highlighting its potential, challenges, and limitations.

Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, MEDLINE, and Embase, to identify relevant articles published from 2022 onwards. The inclusion criteria focused on studies evaluating the use of ChatGPT in retinal healthcare.

Clinical validation of a smartphone-based retinal camera for diabetic retinopathy screening.

Aims: This study aims to compare the performance of a handheld fundus camera (Eyer) and standard tabletop fundus cameras (Visucam 500, Visucam 540, and Canon CR-2) for diabetic retinopathy and diabetic macular edema screening.

Methods: This was a multicenter, cross-sectional study that included images from 327 individuals with diabetes. The participants underwent pharmacological mydriasis and fundus photography in two fields (macula and optic disk centered) with both strategies.