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(1) Underlying Diabetic Retinopathy (DR) is the primary cause of poor vision in young adults. There are automatic image reading systems that can aid screening for DR. (2) Methods: Using our automatic reading system we have counted the number of microaneurysms and hemorrhages in the four quadrants of the ETDRS grid and evaluated the differences between them according to the type of DR. The study was carried out using data from two different databases, MESSIDOR and MIRADATASET. (3) Results: The majority of microaneurysms and hemorrhages are found in the temporal and inferior quadrants of the ETDRS grid. Differences are significant with respect to the other two quadrants at p < 0.001. Differences between the type of DR show that severe-DR has a greater number of microaneurysms and hemorrhages in the temporal and inferior quadrant, being significant at p < 0.001. (4) Conclusions: The count of microaneurysms and hemorrhages is higher in the temporal and inferior quadrants in all types of DR, and those differences are more important in the case of severe-DR.

(1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92–0.94) for any DR and 0.90 (95% CI, 0.89–0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient.

Abstract Fuzzy random forests are well-known machine learning classification mechanisms based on a collection of fuzzy decision trees. An advantage of using fuzzy rules is the possibility to manage uncertainty and to work with linguistic scales. Fuzzy random forests achieve a good classification performance in many problems, but their quality decreases when they face a classification problem with imbalanced data between classes. In some applications, e.g., in medical diagnosis, the classifier is used continuously to classify new instances. In that case, it is possible to collect new examples during the use of the classifier, which can later be taken into account to improve the set of fuzzy rules. In this work, we propose a new iterative method to update the set of trees in the fuzzy random forest by considering trees generated from small sets of new examples. Experiments have been done with a dataset of diabetic patients to predict the risk of developing diabetic retinopathy, and with a dataset about occupancy of an office room. With the proposed method, it has been possible to improve the results obtained when using only standard fuzzy random forests.

(1) Underlying Diabetic Retinopathy (DR) is the primary cause of poor vision in young adults. There are automatic image reading systems that can aid screening for DR. (2) Methods: Using our automatic reading system we have counted the number of microaneurysms and hemorrhages in the four quadrants of the ETDRS grid and evaluated the differences between them according to the type of DR. The study was carried out using data from two different databases, MESSIDOR and MIRADATASET. (3) Results: The majority of microaneurysms and hemorrhages are found in the temporal and inferior quadrants of the ETDRS grid. Differences are significant with respect to the other two quadrants at p < 0.001. Differences between the type of DR show that severe-DR has a greater number of microaneurysms and hemorrhages in the temporal and inferior quadrant, being significant at p < 0.001. (4) Conclusions: The count of microaneurysms and hemorrhages is higher in the temporal and inferior quadrants in all types of DR, and those differences are more important in the case of severe-DR. [ABSTRACT FROM AUTHOR]