## 5 Discussion
The ensemble model had better screening performance than the single-CNN models (Figures 2 and 3). However, the risk of misjudging normal features as abnormal remains. The ensemble with the CNN and random forest model improved that risk. The random forest model, which evaluated for disease using the nasal retinal area, thereby improving on CNN models that misrecognized the nasal peripheral retinal structures (Figures 5 and 7). Thus, the ensemble model combining the CNN models trained with OCT images and the random forest model trained in the retinal area can vastly improve disease prediction during an actual eye or health checkup in which only OCT images are acquired. Furthermore, the ensemble with the CNN and random forest model may be useful to clinicians, given its screening accuracy of 0.999 at 0.025 image/s. In this study, the ensemble model showed high screening performance in the single-shot OCT images captured during the actual eye checkups, because the random forest model complements the weaknesses of CNN. These findings suggest that our ensemble model can screen for retinal diseases without requiring retakes in the actual eye checkups. On the other hand, we have been concerned that the screening performance will be degraded when our ensemble model is applied for actual in-person eye checkups because we excluded OCT images in which the ophthalmologists had difficulty determining the disease by reading the images alone. Therefore, the accuracy of our ensemble model during actual eye checkups will need to be confirmed in a future investigation. [PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON] and [PERSON] conceived the study and designed the experiments. [PERSON], [PERSON], [PERSON] ([PERSON]) and [PERSON] labeled the dataset. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the manuscript. All authors have read and agreed to the published version of the manuscript. Funding:Grants-in-Aid supported this work for Early-Career Scientists, Scientific Research (A) and (B), Challenging Exploratory Research, Japan Society for the Promotion of Science [18H04116 (M.H. ), 19K09928 (S.U. ), 19K09989 (S.U. ), 19K20728 (M.H. ), 19K21783 (M.H. ), 20K04271 (M.H. ), 22K18231 (M.H. ), and 22H00539 (M.H.) respectively]; Charitable Trust Fund for Ophthalmic Research in Commemoration of Santen Pharmaceutical's Founder (M.H. ); Takeda Science Foundation (M.H. ); Nakatani Foundation (M.H. ); Inamori Foundation (M.H. ); and Aichi Health Promotion Foundation (S.U.). Institutional Review Board Statement: This investigation adhered to the tenets of the World Medical Association Declaration of Helsinki. The study was approved by the Institutional Review Board of Nagoya University (Approval No. 2017-0283) and Teikyo University (Approval No. 18-161). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data that support the findings of this study are available from the corresponding author, M.H., upon reasonable request. Conflicts of Interest: [PERSON], (P); [PERSON]. [PERSON], (P); [PERSON], (P); [PERSON], (P); [PERSON], (P); [PERSON], None; [PERSON], None; [PERSON], (P). References References
* (1) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Prevalence, Pattern and Risk Factors of Retinal Diseases among an Elderly Population in Nepal: The Bhaktapur Retina Study. _Clin. Ophthalmol._**2020**, _14_, 2109-2118. [CrossRef]
* (2) [PERSON]; [PERSON]. The Prevalence of Age-Related Eye Diseases and Visual Impairment in Aging: Current Estimates. _Investig. Ophthalmol. Vis. Sci._**2013**, _54_, ORSF5. [CrossRef] [PubMed]
* (3) [PERSON] Economic Cost of Visual Impairment in Japan. _Arch. Ophthalmol._**2010**, _128_, 766. [CrossRef] [PubMed]
* (4) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Current status of eye examinations for adults and local government initiatives. _J. Jpn. Ophthalmol. Assoc._**2017**, _88_, 3-22.