The increasing incidence of diabetes mellitus (DM) in modern society has become a serious issue. DM can also lead to several secondary clinical complications. One of these complications is diabetic retinopathy (DR), which is the leading cause of new cases of blindness for adults in the United States. While DR can be treated if screened and caught early in progression, the only currently effective method to detect symptoms of DR in the eyes of DM patients is through the manual analysis of fundus images. Manual analysis of fundus images is time-consuming for ophthalmologists and can reduce access to DR screening in rural areas. Therefore, effective automatic prescreening tools on a cloud-based platform might be a potential solution to that problem. Recently, deep learning (DL) approaches have been shown to have state-of-the-art performance in image analysis tasks. In this study, we established a research PACS for fundus images to view DICOMized and anonymized fundus images. We prototyped a deep learning engine in the PACS server to perform prescreening classification of uploaded fundus images into DR grade. We fine-tuned a deep convolutional neural network (CNN) model pretrained on the ImageNet dataset by using over 30,000 labeled image samples from the public Kaggle Diabetic Retinopathy Detection fundus image dataset<sup>6</sup>. We linked the PACS repository with the DL engine and demonstrated the output predicted result of DR into the PACS worklist. The initial prescreened result was promising and such applications could have potential as a “second reader” with future CAD development for nextgeneration PACS.