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Diabetes research and clinical practice

October 14, 2026 · via Europe PMC

Effectiveness of technology-enabled teleophthalmology and emerging retinal imaging techniques for diabetic retinopathy screening: a systematic review and meta-analysis.

Chen KY, Chan HC, Chan CM.

ReviewAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Technology-enabled teleophthalmology and advanced retinal imaging improved diabetic retinopathy screening uptake and adherence, with supportive economic evaluations.

What was studied

Systematic review and meta-analysis of 33 studies, searching six databases to 23 May 2026, covering implementation studies, diagnostic accuracy and economic evaluations, with GRADE certainty assessment.

Key findings

  • Teleophthalmology increased screening uptake (RR 1.966, 95% CI 1.451-2.664; I2 = 75.2%) and adherence to eye care (OR 2.464, 1.477-4.112)
  • Pooled ungradable-image rate was 7.4%
  • Advanced retinal imaging had pooled sensitivity 97.59% and specificity 91.22% for vascular lesion detection
  • Economic evaluations supported cost-effective or cost-saving screening

Clinical relevance

Supports integrating teleophthalmology and advanced imaging into scalable screening pathways.

Who should care

Endocrinology

Limitations

Substantial heterogeneity (I2 up to 88.7%), wide confidence intervals, and a mix of study designs; GRADE ratings are not given in the abstract.

Tech-enabled screening raises participation, though pooled estimates vary widely.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Introduction

Diabetic retinopathy (DR) is a major microvascular complication of diabetes and causes preventable visual loss. Technology-enabled screening can extend retinal assessment beyond specialist settings and strengthen screening, referral, and follow-up across diabetes care pathways.

Methods

We systematically reviewed randomized and non-randomized implementation studies, diagnostic-accuracy and comparative retinal-imaging studies, and economic evaluations of technology-enabled DR screening. PubMed, EMBASE, Cochrane Library, Scopus, Web of Science, and Google Scholar were searched from inception to 23 May 2026 without language restriction. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment. Random-effects meta-analyses synthesized comparative and proportional outcomes, and GRADE assessed certainty.

Results

Thirty-three studies were included. Teleophthalmology increased DR screening uptake/completion versus usual care or pre-implementation practice (RR 1.966, 95% CI 1.451-2.664; I2 = 75.2%) and improved adherence to recommended eye care (OR 2.464, 95% CI 1.477-4.112; I2 = 66.47%). The pooled ungradable-image rate was 7.4% (95% CI 3.0%-17.1%; I2 = 59.17%). Advanced retinal imaging showed pooled sensitivity of 97.59% (95% CI 94.50%-100.00%; I2 = 0%) and specificity of 91.22% (95% CI 78.46%-100.00%; I2 = 88.7%) for prespecified vascular lesion detection. OCTA demonstrated a pooled eye-level retinal neovascularization detection yield of 63.6% (95% CI 49.9%-75.4%; I2 = 46.29%). Economic evaluations supported cost-effective or cost-saving technology-enabled screening across assessed health systems.

Conclusion

Technology-enabled DR care improves screening participation, adherence, and health-system efficiency, supporting integration of teleophthalmology and advanced imaging into scalable diabetes complication-management pathways.

DOI

10.1016/j.diabres.2026.113610

PMID

42874783

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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