摘要：This research focuses on the automatic detection and grading of microaneurysms in fundus images of diabetic retinopathy using artificial intelligence deep learning algorithms. By integrating multi-source fundus image data and undergoing a rigorous preprocessing workflow, a hybrid deep learning model architecture combining a modified U-Net and a residual neural network was adopted for the study. The experimental results show that the model achieved an accuracy of [X]% in microaneurysm detection, with a recall rate of [Y]% and a precision rate of [Z]%. In terms of grading diabetic retinopathy, the Cohen’s kappa coefficient for agreement with clinical grading was [K], and there were specific sensitivities and specificities for each grade. Compared with traditional methods, this model has significant advantages in processing speed and result consistency. However, it also has limitations such as insufficient data diversity, difficulties for the algorithm in detecting microaneurysms in severely hemorrhagic images, and high computational costs. The results of this research are of great significance for the early screening and clinical diagnosis decision support of diabetic retinopathy. In the future, it is necessary to further optimize the data and algorithms and promote clinical integration and telemedicine applications.