Deep Learning-Based Models for Sickle Cell Anemia Characterization in Retinal Fundus Images

Sickle cell disease (SCD) is a severe hereditary disorder that affects multiple systems and compromises blood flow, potentially leading to serious complications such as tissue damage and vision loss. Despite treatment developments, early detection is essential, even more so in regions with limited resources. In this context, clinical manifestations observed in ocular fundus images, such as vascular tortuosity, provide the opportunity to detect SCD non-invasively by applying artificial intelligence algorithms and techniques. This study analyzes the potential of deep learning methods to detect SCD using ocular fundus images and to identify relevant retinal patterns in this disorder, which leads to improved comprehension and clinical management of the disease. To this end, we used ocular fundus images from SCD patients and healthy controls to train and evaluate several models of neural networks models, including CNN and a hybrid CNN-Transformer Vision model. In addition, activation maps were built to identify the most relevant retinal characteristics for the classification problem. ResNet-50 and EfficientNet-b0 models showed better performance in the F1 score metric, getting 88 % and 83 % values, respectively. The activation maps analyze highlighted vascular tortuosity as an important feature of disorder detection. Notwithstanding certain limitations, such as the size of the data set or the variability between the images, the results obtained are promising. Solving these problems could improve the effectiveness of the models for the detection and characterization of this disorder.