## 4 Experiment 2
### Results  
The sum of the retinal and choroidal areas was significantly thicker in the abnormal eyes than in the normal eyes in segments 3 and 4 (\(p\) < 0.001) and was significantly thinner in the abnormal eyes than in the normal eyes in segment 5 (\(p\) < 0.001; Figure 7). The accuracies of each segment as evaluated by the random forest model were 83% for segment 1 (abnormal 47/50, normal 36/50; Figure 8A), 87% for segment 2 (abnormal 48/50,  
Figure 6: All original OCT images (**A**) had 20% of pixels removed from the right and left edges (red line and red arrows) to avoid (**B**) depression of the optic disk. (**C**) The OCT images were divided into five sections, each with a resolution of 123 \(\times\) 1176 pixels. The peripheral temporal retina, temporal perimacular area, central macular area, nasal perimacular area, and peripheral nasal retina were defined as segments 1, 2, 3, 4, and 5, respectively. (**D**) Each section was binarized using the discriminant analysis method. (**E**) Morphological closing was applied to these segment images to pad the dark area related to the inner retinal layer and choroidal vessels. The sum of the retinal and choroidal areas was then calculated. Abbreviations: OCT, optical coherence tomography; ML, machine learning; seg, segment. normal 39/50; Figure 8B), 96% for segment 3 (abnormal 50/50, normal 46/50; Figure 8C), 88% for segment 4 (abnormal 47/50, normal 41/50; Figure 8D), and 89% for segment 5 (abnormal 45/50, normal 44/50; Figure 8E). Figure 8: Classification of each segment in the ML model. The red and green dots and areas indicate the abnormal and normal eyes classified by the ML model in each segment. The accuracies of each segment were (**A**) 83% for segment 1 (peripheral temporal retina), (**B**) 87% for segment 2 (temporal perimacular area), (**C**) 96% for segment 3 (central macular area), (**D**) 88% for segment 4 (nasal perimacular area), and (**E**) 89% for segment 5 (peripheral nasal retina). Abbreviation: ML, machine learning. Figure 7: Sum of the retinal and choroidal areas in each segment. The peripheral temporal retina, temporal perimacular area, central macular area, nasal perimacular area, and peripheral nasal retina were defined as segments 1, 2, 3, 4, and 5, respectively. The sum of the retinal and choroidal areas was significantly thicker in abnormal eyes than in normal eyes in segments 3 and 4. Furthermore, the retinal and choroidal areas were significantly thinner in abnormal eyes than in normal eyes in segment 5. Abbreviation: seg, segment. ***\(p\) < 0.001. The random forests, ensemble with CNNs, and ensemble with CNNs and random forest models showed accuracies of 89% (abnormal 45/50, normal 44/50; Table 2), 98.0% (abnormal 50/50, normal 48/50), and 99% (abnormal 50/50, normal 49/50), respectively (Figure 9). The screening performances of the random forests, ensemble with CNNs, and ensemble with CNNs and random forest models were 0.962 (95% confidence interval [CI], 0.922-1.000), 0.998 (95% CI, 0.989-1.000), and 0.999 (95% CI, 0.996-1.000), respectively (Figure 10). The random forest model prediction was unsuccessful when the retinal and choroidal areas were underestimated during image processing (Figure 11). The ensemble with the CNNs and random forest model analyzed 0.025 image/s. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Disease** & **Test Data** &
\begin{tabular}{c} **Random Forest** \\ **Failure** \\ \end{tabular} \\ \hline AMD & 5 & \\ CSC & 1 & \\ ERM & 6 & 1 \\ Macular edema & 14 & 2 \\ Macular hole & 3 & \\ High myopia & 5 & \\ Post-operation & 2 & \\ RP & 12 & 2 \\ RRD & 1 & \\ VMTS & 1 & \\ \hline Total & 50 & 5 \\ \hline AMD, age-related macular degeneration; CSC, central serious chorioretinopathy; ERM, epiretinal membrane; OCT, optical coherence tomography; RP, retinitis pigmentosa; RRD, rhegmatogenous retinal detachment; VMTS, vitreomacular traction syndrome. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Failure data in the random forest model.