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Abstract

PURPOSE: Diabetic retinopathy screening in low- and middle-income countries is limited by restricted access to specialized care. Portable retinal cameras offer a practical alternative; however, image quality – affected by mydriasis – directly influences the performance of artificial intelligence models. This study evaluated the effect of mydriasis on image gradability and AI-based diabetic retinopathy detection in real-world, resource-limited settings.
METHODS: The proportions of gradable images were compared between mydriatic and non-mydriatic groups. Generalized estimating equations were used to identify factors associated with image gradability, including age, sex, race, diabetes duration, and systemic hypertension. A ResNet-200d model was trained on the mobile Brazilian Ophthalmological dataset and externally validated on both mydriatic and non-mydriatic images. Model performance was evaluated using accuracy, F1 score, area under the curve, and confusion matrix metrics. Sensitivity differences were assessed using the McNemar test, and area under the curves were compared using DeLong's test. The Youden index was used to determine optimal classification thresholds. Agreement between macula- and disc-centered images was analyzed using Cohen's κ.
RESULTS: The mydriatic group demonstrated a higher proportion of gradable images compared with the non-mydriatic group (82.1% vs. 55.6%; p<0.001). In non-mydriatic images, lower gradability was associated with systemic hypertension, older age, male sex, and longer diabetes duration. The AI model achieved better performance in mydriatic images (accuracy, 85.15%; area under the curve, 0.94) than in non-mydriatic images (accuracy, 79.68%; area under the curve, 0.93). The McNemar test showed a significant difference in sensitivity (p=0.0001), whereas DeLong's test revealed no significant difference in area under the curve (p=0.4666). The Youden index indicated that optimal classification thresholds differed based on mydriasis status. Agreement between image fields was moderate to substantial and improved with mydriasis.
CONCLUSION: Mydriasis significantly improves image gradability and enhances AI performance in diabetic retinopathy screening. Nonetheless, in low- and middle-income countries where pharmacologic dilation may be impractical, optimizing model calibration and thresholding for non-mydriatic images is essential to ensure effective AI implementation in real-world clinical environments.

Keywords: Artificial intelligence; Bias; Diabetic retinopathy; Portable camera; Retina