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.

本研究的目的是建立一个机器学习模型，该模型可以使用从电子健康记录 (EHR) 中提取的临床属性和从照片中提取的图像特征来预测糖尿病相关足部溃疡的愈合情况。临床信息和照片是在一个学术足病伤口诊所在三年内收集的。从伤口照片中提取了来自 ResNet-50 的全局平均池化层的手工制作的颜色和纹理特征以及基于深度学习的特征。然后训练随机森林 (RF) 和支持向量机 (SVM) 模型进行预测。对于最终伤口愈合的预测，仅使用手工成像特征构建的模型优于仅使用临床或深度学习特征构建的模型。使用所有特征训练的模型与使用手工成像特征训练的模型相比表现得比较好。利用在研究环境之外拍摄的智能手机和平板电脑照片有望预测糖尿病相关足部溃疡的预后。