## 2 Related Work  
You Only Look Once (YOLO) is a state-of-the-art convolutional neural network (CNN) architecture developed by Ultralytics for training and deploying highly accurate AI models [22]. YOLO is an efficient object detection algorithm that performs both localization and classification in a single forward pass [23]. Version 8 of YOLO sets new standards in real-time detection and segmentation, with enhanced capabilities for detecting objects of varying sizes and improved identification efficiency compared to previous versions. Additionally, YOLOv8 is accessible for use online [24]. Many researchers have chosen the YOLO network for object detection over other network frameworks due to its faster speed and higher accuracy [25; 26; 27]. Our previous experience with YOLO versions 8 and 9 is well documented in [28; 29; 30], where we focused on optic nerve segmentation and diabetic retinopathy feature detection. For the first time, we decided to adopt YOLOv8 to perform a classification task aimed at diagnosing myopia, with the preliminary results of our experiment described in [31]. Other studies have explored different approaches to extract novel information, such as refractive error and the risk of refractive error progression from retinal fundus images. In 2018, a study [19] used 226,870 retinal fundus images from the UK Biobank and Age-Related Eye Disease Study (AREDS) clinical trials to train a deep learning algorithm to predict refractive error. The researchers employed a residual network (ResNet) to learn predictive image features that correlate with the development of refractive error, and a soft-attention layer to identify and prioritize the most crucial regions of the image for accurate predictions. The model was validated on approximately 40,000 images, achieving a mean absolute error of 0.56 dipoters for the UK Biobank dataset and 0.91 dipoters for the AREDS dataset in estimating the spherical equivalent. This study was the first to explore the feasibility of calculating the dioptric value of refractive error from retinal fundus images alone. More recently, in 2023, a group of researchers from Singapore developed a deep learning (DL) system to identify children at risk of developing high myopia and its associated eye-threatening complications [20]. They used 7456 baseline fundus images as primary validation data and 821 images for external validation, with and without clinical data such as age, gender, race, parental myopia, and baseline spherical equivalent refraction (SER). Three distinct algorithms were developed to predict the 5-year risk of high myopia development: one based on image data alone, one based on clinical data alone, and one combining both (image + clinical). All three models, based on a DenseNet-121 neural network, achieved clinically acceptable performance, with the mixed model showing the best predictive results, yielding an area under the curve (AUC) of 0.97 in the primary dataset and 0.97-0.98 in the test dataset. This method can support eye care practitioners and volunteers in myopia screening campaigns and can be easily implemented in primary healthcare settings without requiring costly equipment or professionals to measure clinical data such as axial length and cycloplegic refraction. Another study [32] focused on classifying myopia using deep learning methods with different CNN architectures, such as the VGG16, VGG19, and InceptionV3 models. This objective is closely aligned with the one described in the present work. The authors used 495 retinal fundus images from the Retinal Fundus Multi-Disease Image Dataset (RFMID) [33] and applied data augmentation to expand their dataset to 2025 images, divided in two classes: normal and myopic. They conducted a training session to help their algorithms distinguish between the two classes, followed by validation and testing sessions. The training parameters included an image size of 224 \(\times\) 224 pixels, a batch size of 32, 20 epochs, and the Adam optimizer.