ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Health Technology Implementation
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1547045
This article is part of the Research TopicEfficient Artificial Intelligence in Ophthalmic Imaging – Volume IIView all 8 articles
Improving diabetic retinopathy screening using Artificial Intelligence: design, evaluation and before-and-after study of a custom development
Provisionally accepted
Imanol Pinto1*
Álvaro Olazarán1David Jurío1Borja De La Osa1Miguel Sainz1Aritz Oscoz1Jerónimo Ballaz1Javier Gorricho2,3Mikel Galar2,4José Andonegui2,5- 1Health Technology Services, General Directorate of Telecommunications and Digitalization (DGTD). Government of Navarre., Pamplona, Navarre, Spain
- 2Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Navarre, Spain
- 3Navarro Health Service, Pamplona, Navarre, Spain
- 4Institute of Smart Cities (ISC), Public University of Navarre (UPNA), Pamplona, Spain
- 5Ophthalmology Service, University Hospital of Navarre, Pamplona, Spain
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Background: The worst outcomes of diabetic retinopathy (DR) can be prevented by implementing DR screening programs assisted by AI. At the University Hospital of Navarre (HUN), Spain, general practitioners (GPs) grade fundus images in an ongoing DR screening program, referring to a second screening level (ophthalmologist) target patients.Methods: After collecting their requirements, HUN decided to develop a custom AI tool, called NaIA-RD, to assist their GPs in DR screening. This paper introduces NaIA-RD, details its implementation, and highlights its unique combination of DR and retinal image quality grading in a single system. Its impact is measured in an unprecedented before-and-after study that compares 19,828 patients screened before NaIA-RD's implementation and 22,962 patients screened after.Results: NaIA-RD influenced the screening criteria of 3/4 GPs, increasing their sensitivity.Agreement between NaIA-RD and the GPs was high for non-referral proposals (94.6% or more), but lower and variable (from 23.4% to 86.6%) for referral proposals. An ophthalmologist discarded a NaIA-RD error in most of contradicted referral proposals by labeling the 93\% of a sample of them as referable. In an autonomous setup, NaIA-RD would have reduced the study visualization workload by 4.27 times without missing a single case of sight-threatening DR referred by a GP.Conclusion: DR screening was more effective when supported by NaIA-RD, which could be safely used to autonomously perform the first level of screening. This shows how AI devices, when seamlessly integrated into clinical workflows, can help improve clinical pathways in the long term.
Keywords: Diabetic Retinopathy, AI medical device, Decision-support system, deep learning, Before-and-after study
Received: 17 Dec 2024; Accepted: 02 Jun 2025.
Copyright: © 2025 Pinto, Olazarán, Jurío, De La Osa, Sainz, Oscoz, Ballaz, Gorricho, Galar and Andonegui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Imanol Pinto, Health Technology Services, General Directorate of Telecommunications and Digitalization (DGTD). Government of Navarre., Pamplona, Navarre, Spain
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.