## 2 Materials and Methods
### Segmentation Agreement  
Of 366 matched image pairs for both wound and abdomen sites (732 labels in total), the distribution of agreement ranged from 0.35 to 1. Whilst overall the average agreement was 0.74 (standard deviation 0.141), the average agreement for the abdomen was much better (0.85, with standard deviation 0.070) than the average agreement for the wound site (0.63, with standard deviation 0.107). Good segmentation agreement (Jacquard similarity coefficient \(\geq\) 0.7) of mask size and shape was observed for abdomen in all but four image label pairs (Figure 5), indicating that manual segmentation agreement of less than 70% between two observers occurred in just 1.1% of abdomen pairs only. Examples of the best agreement between reviewers in this study are shown in Figure 6 and agreement in the range of 50-70% in Figure 7. We can see from Figure 7 that the four cases of worst agreement in the segmentation of the abdomen ROI occurred mainly due to confounding factors (such as the inclusion of hands for patient 29 at day 15, disjoint temperature areas for patient 10 at day 16, and a colostomy bag in the abdomen ROI image for patient 42 at day 7). Figure 4: ‘Object’ labels identify extent and site of abdominal skin ‘cold regions’ (**A**) and wound ‘cold spot’ clusters, (**B**) (shown in white for clarity in this image). Figure 5: Distribution of segmentation agreement for abdomen masks between two reviewers. Figure 6: Best agreement (over 95%) reveals the potential for near identical mapping of mask outlines at abdomen site. Left column shows temperature scale and thermal image of abdomen of two women, middle column shows segmentation masks produced by reviewer 1, and end column presents segmentation mask produced by reviewer 2. However, distribution of agreement between reviewer 1 and 2 was mainly poor for wound site (Figure 8), with 128/183 (70%) of mask pairs with similarity coefficient \(<\) 0.7 and with 21/183 mask pairs (11.5%) having less than 50% agreement. Good agreement (Jaccard similarity coefficient \(\geq\) 0.7) was observed in just 30% of masks for the wound site. Seven examples for wound agreement varying from poor (A) to good (B) are provided in Figure 9. The main cause of lack of agreement in the segmentation of the wound site ROI appears to be differences in opinion between the two reviewers in regard to how closely to follow temperature profiles, with reviewer 2 choosing to follow the temperature contours more closely during the segmentation process and reviewer 1 choosing to focus on the broader shape of the wound site ROI. This is an area that can be addressed through additional training. Figure 7: Thermal image (with temperature scale, left) and agreement co-efficient for abdomen pairs (reviewer 1 vs reviewer 2), all with coefficients of 50–70% * image shows position of colostomy bag. Figure 8: Distribution of segmentation agreement for wound masks between two reviewers. Figure 9: _Cont._  
Results from the original study [3] using logistic regression between subjectively drawn ROIs for wound and abdomen were calculated at days 2, 7, 15 and 30 after surgery. For each 1 \({}^{\circ}\)C widening of temperature between wound and abdomen sites (wound minus abdomen temperature difference, WATD), there was a two-fold raised odds of SSI at day 2 (odds ratio (OR) 2.25, 95% CI 1.07-5.15; \(p\) = 0.034) with 70.1% of cases correctly classified. Improved model performance was noted at later times after surgery such that by day 7; 78.7% of cases were correctly classified (OR 2.45; 95% CI, 1.13-5.29 \(p\) = 0.023). By comparison, results from the present study revealed that temperature extracted from abdomen and wound masks provided by two reviewers showed variable impact on model performance. For example, at day 2, temperature differences between the two ROIs (WATD) extracted from masks created by reviewer 1 showed 76% accuracy at day 2 (a difference of 5% compared with the original result, 70.9%).