Scan Density Matters: Reproducibility of AI-Derived OCT Biomarkers in Diabetic Macular Edema

Purpose: To determine how optical coherence tomography (OCT) scan density affects quantification of artificial intelligence (AI)-derived structural biomarkers in diabetic macular edema (DME) and to identify density thresholds beyond which biomarker fidelity is compromised. Methods: In this cross-sectional study, 401 DME eyes underwent three same-session OCT acquisitions using 97-, 49-, and 25-B-scan raster protocols on a single device. A CE-certified deep learning pipeline quantified intraretinal fluid (IRF) volume, subretinal fluid (SRF) volume, inflammatory hyperreflective foci (I-HRF), and photoreceptor integrity metrics. Linear mixed-effects models assessed density effects, Bland-Altman analyses quantified fixed and proportional bias, and volumetric thresholds were computed for deviations beyond ±0.10 mm³. Acquisition efficiency integrated biomarker variability and scan time. Results: A total of 9624 biomarker measurements were analyzed with >98% completeness. SRF volume, I-HRF counts, and photoreceptor integrity metrics were stable across scan densities. IRF volume was density-dependent: the 25-scan protocol overestimated IRF relative to 97- and 49-scan acquisitions (mean bias -0.077 and -0.079 mm³; both P < 0.001), whereas 97- and 49-scan measurements were interchangeable. Overestimation increased with fluid burden (IRF threshold ∼1.1 mm³). Although the 25-scan protocol was fastest (10.7 seconds vs. 23.6 seconds and 50.3 seconds), the 49-scan protocol provided the best balance between speed and precision. Conclusions: Most AI-derived OCT biomarkers in DME are robust to reduced scan density, but IRF volume shows increasing error with undersampling. Higher-density scans should be reserved when precise fluid quantification is required. Translational Relevance: Scan density materially influences AI-derived IRF quantification. Identifying practical acquisition thresholds enables protocol standardization while reducing imaging burden in clinical practice and trials.