Telemedical Diabetic Retinopathy Screening in a Primary Care Setting: Quality of Retinal Photographs and Accuracy of Automated Image Analysis
Abstract
Background Screening for diabetic eye disease (DED) and general diabetes care is often separate, which leads to delays and low adherence to DED screening recommendations. Thus, we assessed the feasibility, achieved image quality, and possible barriers of telemedical DED screening in a point-of-care general practice setting and the accuracy of an automated algorithm for detection of DED. Methods Patients with diabetes were recruited at general practices. Retinal images were acquired using a non-mydriatic camera (CenterVue, Italy) by medical assistants. Images were quality assessed and double graded by two graders. All images were also graded automatically using a commercially available artificial intelligence (AI) algorithm (EyeArt version 2.1.0, Eyenuk Inc.). Results A total of 75 patients (147 eyes; mean age 69 years, 96% type 2 diabetes) were included. Most of the patients (51; 68%) preferred DED screening at the general practice, but only twenty-four (32%) were willing to pay for this service. Images of 63 patients (84%) were determined to be evaluable, and DED was diagnosed in 6 patients (8.0%). The algorithm's positive/negative predictive values (95% confidence interval) were 0.80 (0.28-0.99)/1.00 (0.92-1.00) and 0.75 (0.19-0.99)/0.98 (0.88-1.00) for detection of any DED and referral-warranted DED, respectively.Overall, the number of referrals was 18 (24%) for manual telemedical assessment and 31 (41%) for the artificial intelligence (AI) algorithm, resulting in a relati…
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A 2023 study by Najac et al found that overall image quality in telemedical general practitioner-based diabetic eye disease testing with EyeArt was sufficient and would be accepted by both medical assistants and patients in most cases.
“Their study found that the overall image quality achieved in this telemedical general practicioner-based diabetic eye disease testing was sufficient and would be accepted by both medical assistants and patients in most cases.”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [35]
The 2023 study by Najac et al highlighted challenges in ensuring good image quality and integrating the system into the existing workflow.
“However, the study also highlighted challenges in ensuring good image quality and integrating the system into the existing workflow.35,36”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [35]
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Teng, C. W., Patel, S. D., Barkmeier, A. J., Liu, T. Y. A., Myung, D., Henderer, J., Liu, J., Hansen, E., & Al-Aswad, L. A. (2026) · Ophthalmology Science · doi:10.1016/j.xops.2025.100935 · Cited by 23 · see citing works
- Authors
- Wintergerst, M. W. M., Bejan, V., Hartmann, V., Schnorrenberg, M., Bleckwenn, M., Weckbecker, K., & Finger, R. P.
- Affiliations
- University Hospital Bonn (DE)
- Witten/Herdecke University (DE)
- Leipzig University (DE)
- Corresponding author
- Finger, Robert P.
- Venue
- Ophthalmic Epidemiology
- Publisher
- Informa UK Limited
- Published
- 2021-06-20
- Volume
- 29
- Issue
- 3
- Pages
- 286-295
- ISSN
- 0928-6586 (ISSN-L)
- 1744-5086
- OpenAlex
- W3174760049
- Cited by
- 39
- References
- 5
- Citations by year
- 2026: 7 · 2025: 18 · 2024: 4 · 2023: 6 · 2022: 4
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ophthalmic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- diabetic retinopathy screening, diabetic retinopathy, telemedicine, primary care, retinal image quality, automated image analysis, artificial intelligence, type 2 diabetes, patient acceptance, medical assistants, workflow integration
- MeSH terms
- Aged, Artificial Intelligence, Diabetes Mellitus, Type 2, Diabetic Retinopathy, Humans, Mass Screening, Photography, Primary Health Care, Sensitivity and Specificity
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