## 3 Discussion  
In this study cohort of obese women, we have used caesarean section as a model of acute incisional surgery. The main objective, to refine and improve scope for image analysis using AI-based segmentation, has been achieved, in that it is now possible to understand the extent to which the wound infection outcomes observed in the original study are altered by a different set of reviewers following a brief training overview. Manual segmentation of paired regions produced boundary masks, each with a set of per-pixel temperature data. Whilst the abdomen covered a larger area of interest, agreement between reviewers was good with only four masks having less than 70% agreement. By contrast, segmentation of the wound required attention to jagged edges and uneven margins, which clearly exaggerated subjectivity of opinion as to where the boundary between wound and healthy skin lay. This led to the majority of wound pair masks showing poor agreement; 30% of mask pairs only had a similarity coefficient of \(\geq\)0.7. Examining the thermal images and corresponding masks for the least agreement in segmentation of the wound ROI in Figure 8a, a key factor appears to be the ability of the reviewer to closely follow the temperature \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** &
\begin{tabular}{c} **Mean Difference** \\ **between Reviewers (\({}^{\circ}\)C)** \\ \end{tabular} & **Standard Deviation (\({}^{\circ}\)C)** \\ \hline Day 2 Abdomen Mean & 0.04 & 0.04 \\ \hline Day 2 Wound Mean & 0.12 & 0.10 \\ \hline Day 2 ROI Temperature Difference & 0.11 & 0.09 \\ \hline Day 7 Abdomen Mean & 0.07 & 0.07 \\ \hline Day 7 Wound Mean & 0.10 & 0.08 \\ \hline Day 7 ROI Temperature Difference & 0.12 & 0.109 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Difference in ROI features for abdomen and wound ROIs between reviewer 1 and reviewer 2 for data extracted at day 2 and day 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{2}{c}{**Non-SSI**} & \multicolumn{2}{c}{**SSI**} \\ \cline{2-5}  & **Mean** & **Std** & **Mean** & **Std** \\ \hline Day 2 Number of cold spots & 2.8 & 3.5 & 4.0 & 2.9 \\ \hline Day 2 Area of cold spots (pixels) & 48.2 & 71.6 & 75.3 & 51.6 \\ \hline Day 7 Number of cold spots & 3.7 & 4.3 & 3.4 & 3.5 \\ \hline Day 7 Area of cold spots (pixels) & 85.0 & 147.2 & 45.4 & 50.8 \\ \hline Day 15 Number of cold spots & 2.4 & 2.5 & 3.3 & 4.0 \\ \hline Day 15 Area of cold spots (pixels) & 53.2 & 65.5 & 86.4 & 99.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of cold spot features and wound area (pixels) at follow-up points; day 2, 7 and 15 after surgery (n = 50 women). contours when undertaking segmentation of the region of interest. This could potentially be somewhat mitigated by providing more guidance in respect to the segmentation approach that the reviewers should use. However, this does highlight the subjective nature of this segmentation process. Whether a longer training session would have improved the result such that reviewers would have produced masks that were more perfectly aligned is unclear, but what we do see (as shown in Table 1) is that differences in the segmentation process and resultant mask shape and size leads to differences in the temperature values derived from each mask. When comparing both novice reviewers' wound and abdomen segmentation results to produce a delta value (wound minus abdomen temperature, WATD) against the original segmentation by an experienced clinician [3], the confirmed SSI events differed (at day 2) by 5% and 3% for reviewer1 and reviewer2, respectively.