Clinical Chemistry. Principles and Technics.

PURPOSE: The purpose of this study was to evaluate whether explicitly modeling diabetes mellitus (DM) without diabetic retinopathy (DR) as its own stage enables deep learning (DL) to detect early retinal changes for early risk identification of DR severity spectrum. METHODS: We developed 3 DL classification models that explicitly incorporated DM without DR as a distinct stage using 3-class, 4-class, and 6-class staging granularity using 6069 color fundus images from the University of Illinois Chicago Hospital, including 1996 no-DM cases, 1852 DM without DR cases, and 2221 DR cases (516 mild, 220 moderate, 103 severe, and 1382 proliferative DR PDR). We developed segmentation models for the optic nerve head (ONH) and retinal vessels to quantify the impact of these regions on classification performance through targeted perturbations. We also examined spatial changes in retinal features across DR stages by measuring the alignment between DL saliency maps and ONH location. RESULTS: For the 3-class model, areas under the curve (AUCs) were 92.2% (no-DM), 80.3% (DM without DR), and 74.1% (mild DR). For the 4-class model, AUCs were 94.0% (no-DM), 71.9% (DM without DR), 61.5% (mild DR), and 80.3% (referable DR). For the 6-class model, AUCs were 94.0% (no-DM), 65.7% (DM without DR), 65.6% (mild DR), 58.9% (moderate DR), 58.6% (severe DR), and 76.1% (PDR). Vessel perturbations reduced performance by 16% to 31% across models, and greater DR severity was associated with increased saliency-to-ONH distances (Pearson r = 0.69-0.72, P < 0.001). CONCLUSIONS: Explicitly modeling diabetes without retinopathy improved early-stage discrimination and revealed feature-reliance shifts with DR severity. Vessel- and saliency-based analyses identified subtle retinal changes preceding clinical DR. TRANSLATIONAL RELEVANCE: Treating diabetes without retinopathy as its own stage may enhance early DR risk identification and aid development of clinically useful artificial intelligence (AI) tools.