Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers

Abstract

The objective of this study was to build a machine learning model that can predict healing of diabetes-related foot ulcers, using both clinical attributes extracted from electronic health records (EHR) and image features extracted from photographs. The clinical information and photographs were collected at an academic podiatry wound clinic over a three-year period. Both hand-crafted color and texture features and deep learning-based features from the global average pooling layer of ResNet-50 were extracted from the wound photographs. Random Forest (RF) and Support Vector Machine (SVM) models were then trained for prediction. For prediction of eventual wound healing, the models built with hand-crafted imaging features alone outperformed models built with clinical or deep-learning features alone. Models trained with all features performed comparatively against models trained with hand-crafted imaging features. Utilization of smartphone and tablet photographs taken outside of research settings hold promise for predicting prognosis of diabetes-related foot ulcers.

Keywords: Clinical prediction model; Diabetic foot ulcers; Electronic health records; Image processing; Machine learning; Photographs.