| AI | artificial intelligence |
| AFS | abrasion foot sores |
| AUC | area under the receiver operating characteristic curve |
| ANN | artificial neural network |
| BPNN | backpropagation neural network |
| BIM | body mass index |
| CNN | convolutional neural network |
| DM | diabetes mellitus |
| DF | diabetic foot |
| DFU | diabetic foot ulcer |
| DFS | diabetic foot sores |
| DFW | diabetic foot wounds |
| DT | decision tree |
| DICE | dice similarity coefficient |
| DFUC | diabetic foot ulcer challenge |
| EHR | electronic health record |
| EMR | electronic medical record |
| ELM | extreme learning machine |
| FCL | fully connected layer |
| FID | Fréchet inception distance |
| GA | genetic algorithm |
| GAN | generative adversarial network |
| Grad-CAM | gradient-weighted class activation mapping |
| HbA1C | hemoglobin 1C |
| IoU | intersection over union |
| KNN | k-nearest neighbor |
| KID | kernel inception distance |
| LR | logistic regression |
| LIME | local interpretable model-agnostic explanations |
| ML | machine learning |
| mAP | mean average precision |
| MAE | mean absolute error |
| MLP | multilayer perceptron |
| NB | naive bayes |
| PVD | peripheral vascular disease |
| PAD | peripheral artery disease |
| RF | random forest |
| RL | reinforcement learning |
| ROC | receiver operating characteristic |
| ReLU | rectified linear unit |
| RAE | relative absolute error |
| RMSE | root mean squared error |
| SNN | Siamese neural network |
| SVM | support vector machine |
| SHAP | shapley additive explanations |
| T2DM | type 2 diabetes mellitus |
| TcPO₂ | transcutaneous oxygen pressure |
| XAI | explainable artificial intelligence |