New Perspectives and Future Challenges in Diabetic Retinopathy

A special issue of Diabetology (ISSN 2673-4540).

Deadline for manuscript submissions: 20 June 2026 | Viewed by 540

Special Issue Editor


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Guest Editor
Department of Pharmacotherapy, Meiji Pharmaceutical University, Kiyose, Japan
Interests: diabetic complications; atherosclerosis; vascular endothelial function; epidemiology; bone metabolism; insulin resistance; angiogenesis

Special Issue Information

Dear Colleagues,

This Special Issue, "New Perspectives and Future Challenges in Diabetic Retinopathy", aims to explore the cutting edge of research into this sight-threatening complication of diabetes. Diabetic retinopathy remains a leading cause of blindness, and a deeper understanding of its complex pathophysiology is paramount for developing more effective treatments. We welcome contributions that unveil novel mechanistic insights into its development and progression, identify promising new therapeutic target molecules, and showcase innovative strategies for drug delivery to the retina. This includes, but is not limited to, advancements in areas such as ophthalmic formulations designed to enhance therapeutic efficacy and improve patient compliance through non-invasive routes. By bringing together diverse, cutting-edge perspectives, we hope to illuminate current hurdles and pave the way for future breakthroughs that will ultimately improve patient outcomes and preserve vision. We eagerly anticipate your valuable contributions to this rapidly evolving field.

Prof. Dr. Akifumi Kushiyama
Guest Editor

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Keywords

  • diabetic retinopathy
  • novel pathophysiology
  • therapeutic targets
  • drug delivery systems
  • future therapeutics

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Published Papers (1 paper)

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Research

11 pages, 3022 KB  
Article
A Real-World Comparison of Three Deep Learning Systems for Diabetic Retinopathy in Remote Australia
by Jocelyn J. Drinkwater, Qiang Li, Kerry Woods, Emma Douglas, Mark Chia, Yukun Zhou, Steve Bartnik, Yachana Shah, Vaibhav Shah, Pearse A. Keane and Angus W. Turner
Diabetology 2025, 6(12), 146; https://doi.org/10.3390/diabetology6120146 - 1 Dec 2025
Viewed by 220
Abstract
Background/objective: Deep learning systems (DLSs) may improve access to screening for diabetic retinopathy (DR), a leading cause of vision loss. Therefore, the aim was to prospectively compare the performance of three DLSs, Google ARDA, Thirona RetCADTM, and EyRIS SELENA+, in the [...] Read more.
Background/objective: Deep learning systems (DLSs) may improve access to screening for diabetic retinopathy (DR), a leading cause of vision loss. Therefore, the aim was to prospectively compare the performance of three DLSs, Google ARDA, Thirona RetCADTM, and EyRIS SELENA+, in the detection of referable DR in a real-world setting. Methods: Participants with self-reported diabetes presented to a mobile facility for DR screening in the remote Pilbara region of Western Australia, which has a high proportion of First Nations people. Sensitivity, specificity, and other performance indicators were calculated for each DLS, compared to grading by an ophthalmologist adjudication panel. Results: Single field colour fundus photographs from 188 eyes of 94 participants (51% male, 70% First Nations Australians, and mean ± SD age of 60.3 ± 12.0 years) were assessed; 39 images had referable DR, 135 had no referable DR, and 14 images were ungradable. The sensitivity/specificity of ARDA was 100% (95% CI: 91.03–100%)/94.81% (89.68–97.47%), RetCAD was 97.37% (86.50–99.53%)/97.01% (92.58–98.83%) and SELENA+ was 91.67% (78.17–97.13%)/80.80% (73.02–86.74%). Conclusions: In a small, real-world service evaluation, comprising majority First Nations people from remote Western Australia, DLSs had high sensitivity and specificity for detecting referable DR. A comparative service evaluation can be useful to highlight differences between DLSs, especially in unique settings or with minority populations. Full article
(This article belongs to the Special Issue New Perspectives and Future Challenges in Diabetic Retinopathy)
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