# A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
## 2 Related Work
### Datasets: Fundus Images and Patient's Vision Data  
The vision chart data and fundus images of patients are obtained from 79,798 patients from February 2016 to January 2021 at the Department of Ophthalmology at Gyeongsang National University Changwon Hospital. The procedures used in this study followed the principles of the Declaration of Helsinki. The requirement for obtaining informed patient consent was waived by the institutional review board of Gyeongsang National University, Changwon Hospital (GNUCH 2021-05-007) due to the retrospective nature of the study. The fundus images we used in this study are acquired in BMP files by an automation program in AutoIt. A fundus image is selected after matching the personal ID of a fundus image to a personal integrated vision information record. The original data used in this study are anonymized before its use. In this study, retrieving personal vision information necessary for machine learning is conducted in two stages: coupling fundus images and personal vision information and pre-processing of fundus images. In the first stage, we extract the vision acuity information from the medical charts of 79,798 patients with the keywords 'VA (Vision Acuity)', 'BCVA (Best Corrected VA)', and 'CVA (Corrected VA)' and reshape, for our purpose, personal vision datasets of 60,021 visual acuity information, of which each has a corresponding fundus image. Initially, we have a total of 102,237 fundus images coupled with individual personal VA measurements. We use the personal id and the date of a funds image taken when coupling a fundus image and personal vision data. Ultimately, we obtained 79,800 images by this matching. Furthermore, 55,152 fundus images of them are used as data sets for machine learning. We abstract the classical 11 levels of VA measurements (0.0-1.0, step by 0.1) into four groups as shown in Table 1. Class 1 consists of 2501 images with visual acuity of 0.0 to 0.05, Class 2 consists of 3972 images with 0.1, 0.15, and 0.2, Class 3 consists of 16,104 images with 0.3 to 0.7, and Class 4 consists of 32,575 images with 0.8 to 1.0. Table 1 shows the characteristics of fundus image findings for each level of VA. In addition to fundus image findings, visual acuity is dependent on optical and neural factors such as the sharpness of the retinal image within the eye, the function of the retina, and the interpretative function of the brain. Table 2 shows three fundus images for each VA level and representative findings of each image. It will help to understand the characteristics of each class. \begin{table}
\begin{tabular}{l l l} \hline
**Conventional** & **New** & **Features** \\
**VA Class** & **VA Classes** & \\ \hline \multirow{4}{*}{0.0–0.05} & \multirow{4}{*}{Class 1} & 1. & Macular pigmentation and depigmentation findings \\  & & 2. & Macular bleeding and ischemia findings \\  & & 3. & Severe Peripapillary Atrophy orand Optic Nerve Atrophy \\  & & 4. & Tortuosity and abnormal findings of blood vessels near the macula \\  & & 5. & Overall cloudy fundus picture \\  & & 6. & Partially poorly observed fundus picture \\ \hline \end{tabular}
\end{table} 
Table 1: Our VA classification. Figure 1: A normal fundus photograph of a right eye.