Predictive Artificial Intelligence Models in the Early Identification of Diabetic Retinopathy

This study aims to evaluate the precision of a mathematical system using Artificial Intelligence (AI) in forecasting retinal anomalies linked to Diabetic Retinopathy (DR). The study adopted a quantitative, descriptive, and exploratory approach. A standard sample of 1684 ocular fundus images was analyzed. These images were divided into two groups: Class 0 for healthy eyes and Class 1 for eyes with DR. A finite population model was used to determine the sample size, which came from a publicly available database. Experts in the field validated the results obtained to guarantee the accuracy of the findings. The study used the Vision AI solution to train and test 3,752 publicly available medical images. During the training phase, an independent set of 1,684 medical images that had not been included in the training sample was selected. The sample was then classified into two groups: (1) Class 0 for healthy eyes; and (2) Class 1 for eyes with DR. To evaluate the model’s performance, a statistical analysis was conducted using key metrics such as accuracy, sensitivity, specificity, F1-score, and confusion matrix. The AI-based model demonstrated an accuracy exceeding 90%, with statistically