Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review
Open accessAbstract
Background: Effective screening is a desirable method for the early detection and successful treatment for diabetic retinopathy, and fundus photography is currently the dominant medium for retinal imaging due to its convenience and accessibility. Manual screening using fundus photographs has however involved considerable costs for patients, clinicians and national health systems, which has limited its application particularly in less-developed countries. The advent of artificial intelligence, and in particular deep learning techniques, has however raised the possibility of widespread automated screening. Main text: In this review, we first briefly survey major published advances in retinal analysis using artificial intelligence. We take care to separately describe standard multiple-field fundus photography, and the newer modalities of ultra-wide field photography and smartphone-based photography. Finally, we consider several machine learning concepts that have been particularly relevant to the domain and illustrate their usage with extant works. Conclusions: In the ophthalmology field, it was demonstrated that deep learning tools for diabetic retinopathy show clinically acceptable diagnostic performance when using colour retinal fundus images. Artificial intelligence models are among the most promising solutions to tackle the burden of diabetic retinopathy management in a comprehensive manner. However, future research is crucial to assess the potential clinical deployment, evaluate the cost-effectiveness of different DL systems in clinical practice and improve clinical acceptance.
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In September 2020, the American Medical Association CPT Editorial Panel created code 92229 for retinal imaging with autonomous point-of-care interpretation, allowing institutions adopting AI-guided DR testing to bill for the procedure.
“Earlier in September 2020, the American Medical Association Current Procedural Terminology (CPT) Editorial Panel created code 92229 for retinal imaging with autonomous point-of-care interpretation, which allowed institutions that chose to adopt AI-guided DR testing to be billed for the procedure.”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [19]
The Background and Retina fundus imaging modalities sections discuss AI-based diabetic retinopathy screening and FDA approval, but do not address CPT code 92229 or billing. Searches for the code, autonomous point-of-care interpretation, and AMA/CPT reimbursement found no passage supporting the September 2020 claim. (LLM, conf=0.76)
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Radiomic-Based Features for Diabetic Retinopathy Early Detection Using Deep Neural NetworksPreprint
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A Physically Based Correction for Hot Wires in Wall Proximity
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- Authors
- Lim, G., Bellemo, V., Xie, Y., Lee, X. Q., Yip, M. Y. T., & Ting, D. S. W.
- Affiliations
- National University of Singapore (SG)
- Singapore National Eye Center (SG)
- Corresponding author
- Ting, Daniel S. W.
- Venue
- Eye and Vision
- Published
- 2020-04-14
- Volume
- 7
- Issue
- 1
- Pages
- 21-21
- ISSN
- 2326-0254 (ISSN-L)
- PMC
- PMC7155252
- OpenAlex
- W3017378335
- Cited by
- 96
- References
- 116
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 11 · 2023: 24 · 2022: 12 · 2021: 22 · 2020: 3
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Eye and Vision (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Medical Research Council (1409022)
- SingHealth Foundation Research Grant (FG648S)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- diabetic retinopathy, artificial intelligence screening, fundus photography, ultra-widefield imaging, deep learning, automated retinal analysis, retinal image analysis, clinical implementation of AI, cost-effectiveness
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