## 3 Results
### Clinical Utility  
The clinical utility framework (Equation (17)) quantitatively evaluates the translational value of OCT classification models by harmonizing diagnostic accuracy with clinical decision priorities. Table 2 demonstrates this integration through weighted combinations of positive predictive value (PPV), negative predictive value (NPV), and F1 scores, governed by the coefficients \(\omega_{1}\) = 0.1, \(\omega_{2}\) = 0.6, and \(\omega_{3}\) = 0.3. These weights were deliberately calibrated to address two critical clinical imperatives: (1) NPV--centric emphasis (\(\omega_{2}\) = 0.6);  
Figure 4: Comparative ROC curves demonstrating diagnostic discrimination in retinal OCT classification; the figure shows ROC curves for abnormal (Class 1) and normal (Class 0) retinal OCT classifications, with AUC values of 0.98 for both classes. Microaveraged performance achieved near-perfect discrimination (AUC 0.99), while macroaveraging maintained robust multiclass separation (AUC 0.98). The steep trajectory toward the top-left quadrant reflects optimized sensitivity–specificity equilibrium, prioritizing minimal false negatives in sight-threatening conditions like AMD and DME. Curves confirm clinical calibration of models to balance early pathology detection (94.06% sensitivity) with reliable normal scan identification (94.61% specificity). The heightened weighting of NPV reflects the clinical necessity to minimize false-negative diagnoses in medical pathology detection. For conditions like diabetic macular edema or choroidal neovascularization, missed diagnoses (false negatives) carry greater patient risk than false alarms, as delayed treatment can lead to irreversible vision loss. The \(\omega_{2}\) = 0.6 allocation ensures predictive models prioritize accurate exclusion of disease, reducing unnecessary follow-up burdens while safeguarding against underdiagnosis. (2) PPV and F1 balance (\(\omega_{1}\) = 0.1, \(\omega_{3}\) = 0.3): The smaller but non-zero weights for PPV (\(\omega_{1}\) = 0.1) and F1 (\(\omega_{3}\) = 0.3) maintain equilibrium between confirming true pathologies (PPV) and balancing precision/recall trade-offs (F1). This configuration acknowledges that while ruling out disease is paramount, definitive identification of true positives remains essential for initiating targeted therapies. Table 2 illustrates the operationalization of these priorities. The unoptimized model achieves a total clinical utility of 0.937 (vs. 0.934 with optimization), driven by NPV's dominant contribution (0.570 vs. 0.564 with optimization). Despite PPV's lower weight, its retained inclusion ensures clinicians receive actionable confirmatory signals when pathologies are detected--a safeguard against over-reliance on NPV alone. The emphasis on NPV (\(\omega_{2}\) = 0.6) in our clinical utility framework reflects the critical importance of minimizing false negatives in sight-threatening retinal conditions such as choroidal neovascularization or diabetic macular edema, where delayed treatment can lead to irreversible vision loss. For pathologies where false negatives are less critical (e.g., benign or slowly progressing conditions), this weighting configuration maintains