## 3 Experiment 1
### Results 1  
Table 1 showed the abnormal OCT images in the test dataset.  
The accuracies of the CNN models with ResNet-152, DenseNet-201, and EfficientNet-B7, and the ensemble model were 95.0% (abnormal 49/50, normal 46/50), 95.0% (abnormal 49/50, normal 46/50), 96.0% (abnormal 48/50, normal 48/50), and 98.0% (abnormal 50/50, normal 48/50), respectively (Figure 2). The AUCs of the CNN models with ResNet-152, DenseNet-201, and EfficientNet-B7, and the ensemble model were 0.989 (95% confidence interval [CI], 0.968-1.000), 0.997 (95% CI, 0.986-1.000), 0.997 (95% CI, 0.986-1.000), and 0.998 (95% CI, 0.989-1.000), respectively (Figure 3). Although, the screening performance between each model did not significantly differ, the screening performance in the ensemble model was the highest among the four models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Disease** & **Test** & **ResNet** & **DenseNet** & **EfficientNet** \\  & **Data** & **Failure** & **Failure** & **Failure** \\ \hline AMD & 5 & 1 & & \\ CSC & 1 & & & \\ ERM & 6 & & & 1 \\ Macular edema & 14 & & & \\ Macular hole & 3 & & & \\ High myopia & 5 & & & \\ Post-operation & 2 & & & \\ RP & 12 & 1 & 1 \\ RRD & 1 & & & \\ VMTS & 1 & & & \\ \hline Total & 50 & 1 & 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed overview of the abnormal OCT images.  
Figure 2: Confusion matrix in the (**A**) ResNet-152, (**B**) DenseNet-201, (**C**) EfficientNet-B7, and (**D**) ensemble models. The horizontal and vertical labels indicate the prediction by each model and ground truth, respectively. (**A**) ResNet-152 showed an accuracy of 95% (abnormal 49/50, normal 46/50). (**B**) DenseNet-201 showed an accuracy of 95% (abnormal 49/50, normal 46/50). (**C**) EfficientNet-B7 showed an accuracy of 96% (abnormal 48/50, normal 48/50). (**D**) The ensemble model showed an accuracy of 99% (abnormal 50/50, normal 48/50).  
In most cases, the CNN models focused on abnormal images of the retina to predict the disease (Figure 4). The images that the CNN models misjudged did not have remarkable lesions in the inner retinal layers, and in such cases, the CNN models focused on the nasal and temporal retinal regions to make decisions (Figure 5).  
Figure 4: Representative visual explanations of the feature map of the CNN model in the corrected OCT image. The heat maps in (**A**) macular hole, (**B**) macular edema, and (**C**) epiretinal membrane indicate the relative activation intensity of predicting abnormalities in the OCT images. Warm colors indicate areas of high attention for classification. The CNN model made a decision based on the location of the warm color. The CNN model focused on the retinal structural changes in the abnormal eyes. Abbreviations: OCT, optical coherence tomography; CNN, convolutional neural network.  
Figure 3: Diagnostic performances of the ResNet-152 (green), DenseNet-201 (purple), EfficientNet-B7 (blue), and ensemble models (red). The diagnostic performances of the CNN model with ResNet-152, DenseNet-201, and EfficientNet-B7 and the ensemble model were 0.989 (95% CI, 0.968–1.000), 0.997 (95% CI, 0.986–1.000), 0.997 (95% CI, 0.986–1.000), and 0.998 (95% CI, 0.989–1.000), respectively. The diagnostic performance was significantly greater in the ensemble model than in the other CNN models (\(p<0.001\)). Abbreviations: CNN, convolutional neural network; CI, confidence interval.  
ResNet-152 and DenseNet-201 made incorrect classifications for 5/100 images. EfficientNet-B7 was incorrect for 4/100 images. The ensemble model was incorrect for 2/100 images.