Background: Diabetic Macular Edema (DME) is a complication of diabetes which, when untreated, leads to vision loss. Screening for DME is recommended for diabetic patients every 1-2 years, however compliance rates are low. Though there is currently no high-efficacy camera-agnostic system for DME detection, an AI system may improve compliance. Methods: A deep learning model was trained for DME detection using the EyePacs dataset. Data was randomly assigned, by participant, into development (n= 14,246) and validation (n= 1,583) sets. Analysis was conducted at the single image, eye, and patient levels. Model performance was evaluated using sensitivity, specificity, and AUC. Findings: At the patient level, sensitivity of 0.901 (CI 95% 0.879-0.917), specificity of 0.900 (CI 95% 0.883-0.911), and AUC of 0.962 (CI 95% 0.955-0.968) were achieved. At the image level, sensitivity of 0.889 (CI 95% 0.878-0.900), specificity of 0.889 (CI 95% 0.877-0.900), and AUC of 0.954 (CI 95% 0.949-0.959) were achieved. At the eye level, sensitivity of 0.905 (CI 95% 0.890- 0.920), specificity of 0.902 (CI 95% 0.890-0.913), and AUC of 0.964 (CI 95% 0.958-0.969) were achieved. Interpretation: DME can be detected from color fundus imaging with high performance on all analysis metrics. Automatic DME detection may simplify screening, leading to more comprehensive screening for diabetic patients. Further prospective studies are necessary.

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