## 3 Results
### Clinical Utility
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & **Without Optimization** & **With Optimization** \\ \hline Basic Metrics & & \\ \hline True positives (TP) & 11,648 (94.98\%) & 11,534 (94.06\%) \\ True negatives (TN) & 5607 (92.66\%) & 5725 (94.61\%) \\ False positives (FP) & 444 (7.34\%) & 326 (5.39\%) \\ False negatives (FN) & 615 (5.02\%) & 729 (5.94\%) \\ \hline Accuracy & 0.942 & 0.942 \\ Sensitivity/recall & 0.950 & 0.941 \\ Specificity & 0.927 & 0.946 \\ Precision/PPV & 0.963 & 0.973 \\ NPV & 0.901 & 0.887 \\ F1 score & 0.956 & 0.956 \\ \hline Equation (12): \(\text{Score}(m_{\theta})=\alpha_{1}\cdot\text{AUC}+\alpha_{2}\cdot\text{Sensitivity}+\alpha_{3}\cdot\text{Specificity}\) & \\ \hline AUC (from Figure 4) & 0.98 & 0.98 \\ \(\alpha_{1}\cdot\text{AUC}=0.5\cdot\text{AUC}\) & 0.490 & 0.490 \\ \(\alpha_{2}\cdot\text{Sensitivity}=0.3\cdot\text{Sensitivity}\) & 0.285 & 0.282 \\ \(\alpha_{3}\cdot\text{Specificity}=0.2\cdot\text{Specificity}\) & 0.185 & 0.189 \\ Total score & 0.960 & 0.961 \\ \hline Equation (17): Clinical Utility\(=\omega_{1}\cdot\text{PPV}+\omega_{2}\cdot\text{NPV}+\omega_{3}\cdot\text{F1}\) & \\ \hline \(\omega_{1}\cdot\text{PPV}=0.1\cdot\text{PPV}\) & 0.0963 & 0.0973 \\ \(\omega_{2}\cdot\text{NPV}=0.6\cdot\text{NPV}\) & 0.5406 & 0.5322 \\ \(\omega_{3}\cdot\text{F1}=0.3\cdot\text{F1}\) & 0.2868 & 0.2868 \\ Total clinical utility & 0.9237 & 0.9163 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Translational impact of diagnostic models through component-weighted clinical utility scores, emphasizing negative predictive value (NPV) prioritization (\(\omega_{2}\) = 0.6) to minimize false-negative risks in retinal pathology detection. Columns show weighted contributions of positive predictive value (PPV), NPV, and F1 scores for unoptimized and SMOTE-optimized models, with total utility reflecting alignment to ophthalmological decision priorities. clinical value through increased reliability in ruling out disease but creates a specific performance profile. Our analysis demonstrates that while the SMOTE-optimized model achieves 94.06% sensitivity with NPV emphasis, it produces a slightly higher false-positive rate (5.39%) compared to alternative weighting schemes. Adjusting these weights for different pathological contexts offers performance customization: reducing NPV weight for benign conditions would improve specificity and reduce unnecessary follow-ups, increasing PPV weight would optimize confirmatory diagnostics rather than screening functions, and equalizing weights would provide balanced performance across error types. This flexibility enables context-specific optimization, as demonstrated by our cross-pathology testing results (95.09% sensitivity across untrained pathologies), suggesting the current weighting generalizes effectively while maintaining sufficient adaptability for diverse clinical scenarios based on specific risk-benefit considerations. Succinctly speaking, this weighting schema aligns with evidence-based ophthalmological practice where diagnostic certainty in excluding disease (NPV) often supersedes confirmatory precision. By explicitly parameterizing these trade-offs, the framework bridges statistical performance and clinical pragmatism, ensuring models prioritize outcomes most consequential to patient care.