Work

Real-World Evaluation of Artificial Intelligence-Based Diabetic Retinopathy Screening Using the Optomed Aurora Handheld Fundus Camera

Year: 2025

Type: article

Abstract: Diabetic retinopathy (DR) is a common and potentially sight-threatening complication of diabetes. Early detection of DR through screening can prevent visual loss. Handheld fundus cameras combined with artificial intelligence (AI) technology may improve DR screening. We evaluated the Aireen AI algorithm's performance in grading DR in fundus images captured by the handheld Optomed Aurora. Two retina specialists and Aireen graded 624 fundus images for DR. Sensitivity, specificity, and predictive values were measured against the ophthalmologists' grading. Overall, 97% of images were sufficient for DR classification. Aireen demonstrated 94.8% sensitivity, 91.4% specificity, and 92.7% diagnostic accuracy for DR. Aireen showed high diagnostic accuracy in detecting DR in Optomed Aurora images, suggesting its potential for effective screening. The validated use of AI with a handheld fundus camera may streamline the screening process, reduce the burden on health care professionals, and improve access to screening and patient outcomes through enhanced diagnostic accuracy.

Source: Diabetes Technology & Therapeutics

DOI: 10.1177/15209156251369886

Authors: Petri Huhtinen, Anna-Maria Kubin, Kamila Dvořák, Martin Sliva, Jan Bayer, Nina Hautala

Institutions: Oulu University Hospital, University of Oulu, Czech Technical University in Prague

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: Ögonfonden, Silmäsäätiö