Purpose: Diabetic retinopathy (DR) screening in low- and middle-income countries (LMICs) faces challenges due to limited access to specialized care. Portable retinal cameras provide a practical alternative, but image quality, influenced by mydriasis, affects artificial intelligence (AI) model performance. This study examines the role of mydriasis in improving image quality and AI-based DR detection in resource-limited settings. Methods: We compared the proportion of gradable images between mydriatic and non-mydriatic groups and used logistic regression to identify factors influencing image gradability, including age, gender, race, diabetes duration, and systemic hypertension. A ResNet-200d algorithm was trained on the mBRSET dataset and validated on mydriatic and non-mydriatic images. Performance metrics, such as accuracy, F1 score, and AUC, were evaluated. Results: The mydriatic group had a higher proportion of gradable images (82.1% vs. 55.6%, P < 0.001). Factors such as systemic hypertension, older age, male gender, and longer diabetes duration were associated with lower image gradability in non-mydriatic images. Mydriatic images achieved better AI performance, with accuracy (82.91% vs. 79.23%), F1 score (0.83 vs. 0.79), and AUC (0.94 vs. 0.93). Among gradable images, the performance difference was not statistically significant. Conclusion: Mydriasis improves image gradability and enhances AI model performance in DR screening. However, optimizing AI for non-mydriatic imaging is critical for LMICs where mydriatic agents may be unavailable. Refining AI models for consistent performance across imaging conditions is essential to support the broader implementation of AI-driven DR screening in resource-constrained settings.

The authors have declared no competing interest.

This study did not receive any funding.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The project was approved by the UNIFESP Ethics Committee CAAE 33842220.7.0000.5505.

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The scripts underlying this study can be found on our GitHub repository: https://github.com/luisnakayama/portable_mydriasis.