## 3 Results
### Evaluation Metrics and Datasets
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Equation** & **Parameters** \\ \hline Accuracy & Accuracy = \(\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}\) & TP: True Positive, TN: True Negative, FP: False Positive, FN: False Negative \\ \hline F1 Score & F1 Score = 2 \(\times\)\(\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{Recall}}\) & \(\frac{\text{Precision}\ =\ \frac{\text{TP}}{\text{TP}+\text{FN}}}{\text{ Recall}\ =\ \frac{\text{TP}}{\text{TP}+\text{FN}}}\) \\ \hline Dice & Dice\({}_{\text{image}}\) = \(\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\frac{\text{2 TPi}}{\text{TP}+\text{FP}+ \text{FN}}\) & TP: True Positive, FP: False Positive, FN: False Negative \\ \hline IoU & IoU\({}_{\text{image}}\) = \(\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\frac{\text{TPi}}{\text{TP}+\text{FP}+ \text{FNi}}\) & TP: True Positive, FP: False Positive, FN: False Negative \\ \hline AUC & AUC = \(\frac{1}{2}\left(\frac{\text{TP}}{\text{TP}+\text{FN}}\right)+\left(\frac{ \text{TN}}{\text{TN}+\text{FP}}\right)\) & TP: True Positive, TN: True Negative, FP: False Positive, FN: False Negative \\ \hline MAE & MAE = \(\frac{1}{n}\sum_{i=1}^{n}\left|yi-\hat{y^{i}}\right|\) & \(\begin{array}{l}yi:\text{ actual value, }\hat{y^{i}}:\text{ predicted value,}\\ \text{n: total number of data points}\end{array}\) \\ \hline RMSE & RMSE = \(\sqrt{\frac{1}{n}}\sum_{i=1}^{n}\left(yi-\hat{y^{i}}\right)^{2}\) & \(\begin{array}{l}yi:\text{ actual value, }\hat{y^{i}}:\text{ predicted value,}\\ \text{n: total number of data points}\end{array}\) \\ \hline RAE & RAE = \(\frac{\sum_{i=1}^{n}\left|yi-\hat{y^{i}}\right|}{\sum_{i=1}^{n}\left|yi-\hat{y ^{i}}\right|}\), \(\overline{y}\ =\ \frac{1}{n}\sum_{i=1}^{n}yi\) & \(\begin{array}{l}yi:\text{ actual value, }\hat{y^{i}}:\text{ predicted value,}\\ \text{n: total number of data points,}\\ \overline{y}:\text{ mean of all actual values}\end{array}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The equations of performance evaluation metrics for AI models in DFU management. Original Images, Patches, TestSet, and TransferLearning Image. The Original Images folder includes 493 images, while the Patches folder contains 1055 images. The TestSet folder includes 167 images, while the TransferLearning Image folder comprises 959 images. The Chronic Wound Dataset [52] contains 1109 images from 889 patients. Lastly, the FUSeg dataset [53], an extended version of the Chronic Wound Dataset, features 1210 foot ulcer images that researchers gathered over two years for segmentation research. These datasets provide essential data that enhance AI model development. The performances of the reviewed models are summarized in Tables 1-6. However, since the models are trained and tested on different datasets and for various tasks, providing a standardized performance comparison is challenging. For instance, the FUSeg dataset is specifically designed for segmentation tasks, while other datasets focus on classification or prediction. The aim of this section is to highlight the most commonly used metrics for evaluating the performance of AI-based solutions for DFUs.