Utilization of smartphone and tablet camera photographs to predict healing of
diabetes-related foot ulcers.
Authors Kim RB, Gryak J, Mishra A, Cui C, Soroushmehr SMR, Najarian K, Wrobel JS
Submitted By James Wrobel on 11/20/2020
Status Published
Journal Computers in biology and medicine
Year 2020
Date Published 11/1/2020
Volume : Pages 126 : 104042
PubMed Reference 33059239
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.

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