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. 2025 Mar 16;13(6):648. doi: 10.3390/healthcare13060648
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