ArticleScandinavian journal of primary health care2026
The diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care: a prospective validation study.
Article in Scandinavian journal of primary health care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
objectivesThis study investigated the diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care, using ophthalmologist-led screening as the reference standard.
methodsPatients with type 2 diabetes attending routine appointments at 10 primary care clinics underwent AI-assisted screening, followed by re-screening at an ophthalmology clinic. The quality of fundus images captured in primary care was independently assessed, and diagnostic accuracy was evaluated by comparing AI-assisted results with ophthalmologist results, including sensitivity, specificity, PPV, NPV, and AUC. Two analyses were conducted: one including all images and one excluding those of poor quality.
resultsAmong 183 patients (336 images), 18.6% of images were classified as poor quality. When all images were included, the AI-assisted screening achieved a sensitivity of 73.7%, specificity of 90.2%, PPV of 31.1%, NPV of 98.3%, and AUC of 0.82. Excluding poor-quality images improved sensitivity to 80.0%, NPV to 98.7%, and AUC to 0.84. Additional ocular findings unrelated to diabetic retinopathy were observed in 96 patients, including confirmed or non-specific signs of glaucoma, cataract, age-macular degeneration, benign nevus and reduced visual acuity.
conclusionAI-assisted screening in primary care shows potential for clinical application, but further validation in larger populations and improvements in image quality are needed before clinical implementation.
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