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Cited by 39Retinal Imaging and AnalysisArtificial Intelligence in HealthcareRetinal Diseases and Treatments

Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging

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Sobhi, Navid, Sadeghi-Bazargani, Yasin, Mirzaei, Majid, Abdollahi, Mirsaeed, Jafarizadeh, Ali, Pedrammehr, Siamak, Alizadehsani, Roohallah, Tan, Ru-San +2 more·2025·Journal of Diabetes & Metabolic Disorders
DOI 10.1007/s40200-025-01596-7PMC PMC11993533
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Abstract

Background: Diabetes mellitus (DM) increases the risk of vascular complications, and retinal vasculature imaging serves as a valuable indicator of both microvascular and macrovascular health. Moreover, artificial intelligence (AI)-enabled systems developed for high-throughput detection of diabetic retinopathy (DR) using digitized retinal images have become clinically adopted. This study reviews AI applications using retinal images for DM-related complications, highlighting advancements beyond DR screening, diagnosis, and prognosis, and addresses implementation challenges, such as ethics, data privacy, equitable access, and explainability. Methods: We conducted a thorough literature search across several databases, including PubMed, Scopus, and Web of Science, focusing on studies involving diabetes, the retina, and artificial intelligence. We reviewed the original research based on their methodology, AI algorithms, data processing techniques, and validation procedures to ensure a detailed analysis of AI applications in diabetic retinal imaging. Results: Retinal images can be used to diagnose DM complications including DR, neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as to predict the risk of cardiovascular events. Beyond DR screening, AI integration also offers significant potential to address the challenges in the comprehensive care of patients with DM. Conclusion: With the ability to evaluate the patient’s health status in relation to DM complications as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.

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  • Models constructed with clinical data such as electronic health records, laboratory tests, and imaging are the most widely used due to their high accessibility.

    “At the feature level, models constructed with clinical data such as EHR [148, 149], laboratory tests [150, 151], and imaging [152, 153] are the most widely used due to their high accessibility.”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [152]

    The Introduction describes digital medical images as accessible and says many AI systems using retinal images have been developed and clinically adopted. This supports imaging as an accessible model input, but the paper does not establish that imaging-based models are the most widely used. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Sobhi, N., Sadeghi-Bazargani, Y., Mirzaei, M., Abdollahi, M., Jafarizadeh, A., Pedrammehr, S., Alizadehsani, R., Tan, R.-S., Islam, S. M. S., & Acharya, U. R.
Affiliations
  • Tabriz University of Medical Sciences (IR)
  • Deakin University (AU)
  • Duke-NUS Medical School (SG)
  • The University of Sydney (AU)
  • University of Southern Queensland (AU)
Corresponding author
  • Jafarizadeh, Ali
  • Pedrammehr, Siamak
ORCID
Venue
Journal of Diabetes & Metabolic Disorders
Publisher
Springer Science and Business Media LLC
Published
2025-04-12
Volume
24
Issue
1
Pages
104-104
ISSN
2251-6581 (ISSN-L)
DOI
10.1007/s40200-025-01596-7
PMC
PMC11993533
OpenAlex
W4409382716
Cited by
39
References
12
Citations by year
2026: 22 · 2025: 17
FWCI
6.16
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Diabetes & Metabolic Disorders (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • Figshare (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
Deakin University
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
Keywords
diabetes complications, diabetic retinopathy, retinal imaging, artificial intelligence, diabetic neuropathy, diabetic nephropathy, atherosclerotic cardiovascular disease, cardiovascular risk prediction, early detection, explainability, data privacy, equitable access, AI ethics

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