## 5 Discussion  
This study investigated the screening performances of ML models using OCT images obtained from actual eye checkups. The CNN models focused on the structural changes in the retina in abnormal eyes, with accuracy from 95% to 96%, and the screening performance did not differ between each model (Figures 2-4). Our finding is consistent with earlier studies that reported a classification accuracy of a single-CNN model of about 70-95%; this model may also miss some diseases using OCT images [10, 11, 13, 14, 15]. Furthermore,  
Figure 11: Original (**A**–**E**) and post-processing (**A**’-**E**’) OCT images misjudged by the ML model. The ML model misinterpreted epiretinal membrane (**A**), macular edema (**B**,**C**), and retinitis pigmentosa (**D**,**E**). In the OCT images of the epiretinal membrane and macular edema, the inner retinal layer was not filled in with white by morphological transformation. The OCT images of retinitis pigmentosa indicate small retinal and choroidal areas. Abbreviations: OCT, optical coherence tomography; ML, machine learning. Figure 10: Diagnostic performances of the ML (purple), ensemble (blue), and ensemble with ML (red) models. The diagnostic performances of the ML, ensemble, and ensemble with ML models were 0.962 (95% CI, 0.922–1.000), 0.998 (95% CI, 0.989–1.000), and 0.999 (95% CI, 0.996–1.000), respectively. The diagnostic performance was significantly greater in the ensemble model with ML than in both ML and ensemble models (\(p<0.001\)). Abbreviations: ML, machine learning; CI, confidence interval. our findings support the earlier study that the latest CNN model is not necessarily better when performing transfer learning using OCT images [20]. We have considered that the number of classification categories relates to our findings. We developed CNN models for the classification of two categories (abnormal, normal), but did not develop CNN models for the classification of every disease in this study. The binary classification is the simplest classifier in CNN models. Therefore, we expect the differences between CNN models to be more significant for multi-class classifications, such as those for classifying individual retinal diseases. RP was false-negatively predicted, suggesting insufficient training with abnormalities in the retinal pigment epithelium and photoreceptor layer, including interdigitation and ellipsoid zones in the ELM. In cases with no apparent edema in the inner retinal layer, our CNN models tended to predict an abnormality based on the peripheral temporal and nasal retinal shapes (Figure 5). [PERSON] et al. [33] described CNN models trained with OCT images of age-related macular degeneration that focused on the temporal and nasal retinas, and differentiated between progressors and non-progressors. These findings suggest that CNN can detect subtle differences in the morphology of the peripheral temporal and nasal retinal regions, and can therefore differentiate between abnormal and normal eyes or between progressors and non-progressors. The random forest model used the central macular (segment 3) and nasal (segments 4 and 5) areas as bases for determining eye abnormalities, as a significant difference was found between abnormal and normal retinal areas in both segments (Figures 7 and 8). Of the five diseases that were misjudged by the random forest model (Figure 9A), the retinal and choroidal areas were underestimated because the morphological transformation did not fill in the inner retinal layer with white in ERM (Figure 11A,A\({}^{\prime}\)) and macular edema (Figure 11B,B\({}^{\prime}\),C,C\({}^{\prime}\)). Furthermore, the OCT images of RP had small retinal and choroidal areas (Figure 11D,D\({}^{\prime}\),E,E\({}^{\prime}\)). Most diseases were correctly classified by the random forest model, although cases with an underestimated macular area were misclassified. Therefore, the features of the retinal and choroidal areas extracted by dividing the OCT images into five segments were useful.