PurposeTo develop an easily applicable predictor of patients at low risk for diabetic retinopathy (DR). DesignAn experimental study on the development and validation of machine learning models and a novel retinopathy risk score to detect patients at low risk for diabetic retinopathy. SubjectsAll individuals aged 18 years or older who participated in the telemedicine retinal screening initiative through Temple University Health Systems from October 1, 2016 through December 31, 2020. The subjects must have documented evidence of their diabetes mellitus diagnosis as well as a documented hemoglobin A1C recorded in their chart within 6 months of the retinal screening photograph. MethodsThe charts of 1930 subjects (1590 evaluable) undergoing telemedicine screening for DR were reviewed, and 30 demographic and clinical parameters were collected. DR is a dichotomous variable where low risk is defined as no or mild retinopathy using the International Clinical Diabetic Retinopathy severity score. Five machine learning models (MLM) were trained to predict patients at low risk for DR using 1050 subjects and further underwent 10-fold cross validation to maximize its performance indicated by the area under the receiver operator characteristic curve (AUC). Additionally, a novel retinopathy risk score (RRS) is defined as the product of hemoglobin A1c closest to screening and years with diabetes mellitus. RRS was also applied to generate a predictive model. Main Outcome MeasuresThe performance of the trained machine learning models and the RRS model was compared using DeLong’s test. The models were further validated using a separate unseen test set of 540 subjects.The performance of the validation models were compared using DeLong’s test and chi-square tests. ResultsUsing the test set, the AUC for the RRS was not statistically different from four out of five MLM. The error rate for predicting low-risk patients using the RRS was significantly lower than the naive rate (0.097 vs 0.19; p<0.0001), and it was comparable to the error rates of the machine learning models. ConclusionThis novel retinopathy risk score is a potentially useful and easily deployable predictor of patients at low risk for DR.