## 3 Results  
This study included a total of 168 patients, with a mean age of 61.9 \(\pm\) 11.6 years. The cohort was made up of 127 men (75.6%) and 41 women (24.4%). The median duration of having had diabetes was 14.0 years (IQR: 8.0-25.0), with 20 patients (11.9%) diagnosed with type one diabetes, and 148 (88.1%) with type two diabetes. The median glycated hemoglobin level was 55.2 mmol/mol (IQR: 46.4-69.4). The median wound duration was 7.5 weeks (IQR: 2.0-20.0).  
The most common location for DFUs was the central metatarals (32.1%, \(n\) = 54), followed by the lesser toes (23.8%, \(n\) = 40), the fifth metataralsal (21.4%, n = 36), the first metatarsal (14.3%, \(n\) = 24), the hallmark (7.1%, \(n\) = 12), and lastly the rearfoot (1.2%, \(n\) = 2). In terms of ulcer types, 58.3% (\(n\) = 98) were type III B, 35.1% (\(n\) = 59) were type III D, 4.2% (\(n\) = 7) were of type IIB, and the remainder, 2.4% (\(n\) = 4), were type IIID.  
Figure 3a,b outline the validation metrics for the AI model and the experienced clinician, respectively. The tables within these figures present crucial diagnostic performance parameters--these include sensitivity, specificity, predictive values, and likelihood ratios. Moreover, the confusion matrix diagrams display the distribution of classification outcomes within the study population, offering a comparative perspective on the diagnostic accuracy of both these methodologies.  
Additionally, Table 1 illustrates the two-by-two agreement matrix between the AI model and the experienced clinician for the diagnosis of diabetic foot osteomyelitisitis (DFO). The number of cases classified as positive or negative by each method is presented. Observed agreement was assessed using [PERSON]'s kappa coefficient, which yielded a value of \(\kappa=-0.105\) (\(p=0.117\)), indicating poor agreement between both the approaches and suggesting differences in their diagnostic criteria.  
To complement this numerical summary, Figure 4 presents an annotated Venn diagram that visually represents the overlap in positive classifications. The AI model identified 157 cases as positive, and the clinician 139 cases, with 128 classified as positive by both.  
\begin{table}
\begin{tabular}{c c c c} \hline
**AI Model** & **Experienced Clinician—Not** & **Experienced Clinician—Yes** & **Total** \\ \hline Not & 0 & 11 & 11 \\ \hline Yes & 29 & 128 & 157 \\ \hline Total & 29 & 139 & 168 \\ \hline \end{tabular}
\end{table}
Table 1: Agreement between the AI model and the experienced clinician in diagnosing diabetic foot osteomyelitisitis. This table presents the cross-tabulation of classification outcomes, showing the number of cases classified as positive (yes) and negative (not) by both the methods.  
Figure 3: **(a)** The model validation metrics and a confusion matrix representation. This table summarizes the diagnostic performance of the AI model, while the diagram illustrates the distribution of classification outcomes within the study population. **(b)** The experienced clinician validation metrics and a confusion matrix representation. This table summarizes the diagnostic performance of the experienced clinician, while the diagram illustrates the distribution of classification outcomes within the study population.  
The diagram clearly highlights the 29 cases labeled as positive only by the AI model and the 11 cases labeled as positive only by the clinician, offering a visual representation of the discordant classifications.