ArticleBMJ open2025
Ratio of haemorrhagic area to retinal area as a novel indicator for AI-based screening of diabetic retinopathy in type 2 diabetes: a community-based cross-sectional study.
Article in BMJ open, 2025. 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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Abstract
backgroundThe application of artificial intelligence (AI) technology in the screening of diabetic retinopathy (DR) has made significant strides. However, there remains a lack of comprehensive validation and evaluation of AI-derived quantitative indicators in DR screening.
objectiveThis study aims to assess the diagnostic performance of retinal microvascular indicators in the early detection of DR in patients with type 2 diabetes and to identify potential novel indicators for early DR screening. RESEARCH DESIGN AND
methodsThis cross-sectional study included 533 community-recruited patients with type 2 diabetes mellitus who underwent fundus imaging. Based on the results of the fundus examination, the eyes were categorised into non-DR, mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR and severe NPDR groups. AI systems were employed to quantify various retinal microvascular indicators, including microaneurysms (MAs), haemorrhage count (HC), haemorrhagic area (HA), the ratio of HA to retinal area (HA/RA), the ratio of HA to MA (HA/MA) and HC and/or MA (H/MA). Multivariable logistic regression was used to analyse the association between fundus indicators and DR severity, and receiver operating characteristic (ROC) curve analysis was performed to assess the predictive and screening value of these indicators, determining sensitivity, specificity, ROC area under the curve (AUC) and optimal cut-off values.
resultsAmong the 533 participants (mean age 64.03±9.71 years; 51.6% female), the DR prevalence was 10.0%. After adjusting for age, gender, body mass index, hypertension, diabetes duration, glycated haemoglobin levels, smoking and alcohol consumption, multivariable logistic regression indicated that HA/RA (OR 1.873, 95% CI 1.453 to 2.416) and HA/MA (OR 1.115, 95% CI 1.063 to 1.169) were associated with mild NPDR. Similarly, HA/RA (OR 1.928, 95% CI 1.509 to 2.464) and HA/MA (OR 1.165, 95% CI 1.112 to 1.220) were associated with moderate NPDR, and HA/RA (OR 2.435, 95% CI 1.921 to 3.086) and HA/MA (OR 1.171, 95% CI 1.117 to 1.226) were linked to severe NPDR. ROC curve analysis revealed that before adjustment, HA/RA demonstrated the highest screening value for DR, with an AUC of 0.917, sensitivity of 86.14%, specificity of 93.41%, Youden's index of 0.796 and an optimal cut-off value of 0.063. After adjusting for confounding factors, the AUC for HA/RA in diagnosing DR was 0.900, with sensitivity of 83.17%, specificity of 86.28%, Youden's index of 0.695 and an optimal cut-off value of 0.093.
conclusionsThe HA/RA and HA/MA show robust screening performance for early DR. These indicators should be considered for inclusion in AI-based early DR screening systems in the future.
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