Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers

Figure 2

Application of machine learning algorithm in the combined model. Learning phase (above): we randomly select a subpopulation of the total patient group, called the training group, and then use the known clinical diagnosis to split the training group into a DR group and a non-DR group. The clinical diagnosis, the number of MAs on the retina images, and the protein concentration values are the inputs of the machine learning algorithm. The algorithms are able to tell which data patterns are the most characteristic for the DR and non-DR groups. Assessment phase (below): in the following steps, we use the data from the validation group. The number of MAs and the protein concentration values constitute the input of the algorithm, but we do not use the information from clinical diagnosis. The learning algorithm compares the new data to the characteristic patterns that are known from the learning phase and will make its own decision (normal/DR) for each patient as the output of the model. For the assessment of the performance of the model, we compare the output with the known clinical diagnosis.