Automatic Diabetic Foot Prediction Through Fundus Images by Radiomics Features

Current clinical approaches to diabetic foot (DF) treatment mainly rely on clinician vigilance and laboratory test, which have significant limitations, such as the high cost involved in the diagnosis and the high demands for professional skills of clinicians. At present, the research on DF prediction has mainly focused on the regression analysis of clinical data and the recognition based on foot ulcers skin. In view of this situation, we examine the patients’ fundus images to explore an efficient way for DF prediction. In this paper, we have proposed a DF prediction model through fundus images by radiomics features. Twelve kinds of radiomics features are extracted, including a variety of features first applied in the field of medical imaging, describing the information of image texture, direction, phase, and gradient. Subsequently, a two-step feature selection model is put forward for a large number of radiomics features we extracted for the purpose of searching for the best combination. Considering the simplicity and performance of the model, we chose 19 features to train the support vector machine model. The obtained model is evaluated with 5-fold cross validation on abundant clinical data, and the mean prediction performance: area under the curve: 0.9678; sensitivity: 0.9786; specificity: 0.9161; accuracy: 0.9247, showed the excellence of the model prediction. Here, we present a new, noninvasive, and efficient detection means for the automatic prediction of DF, which can help clinicians find potential diabetic foot patients earlier and is expected to be a novel auxiliary diagnostic tool.