## 5 Conclusions  
This study demonstrates the potential of artificial intelligence to support the radiographic diagnosis of diabetic foot osteomyelitisitis (DFO) using a ResNet-50-based deep learning model. The AI system achieved excellent sensitivity and a high positive predictive value, exceeding the clinician's performance in sensitivity, although showing a substantially lower level of specificity. These findings support the usefulness of AI-assisted interpretation as a complementary diagnostic tool in specialized diabetic foot units, where early detection of bone infection is critical for preserving limb integrity.  
While the AI model outperformed the clinician in terms of sensitivity, it showed a substantially lower level of specificity. This trade-off suggests that the model may be useful in high-prevalence or specialist settings as a diagnostic aid, but further refinement is required before broader clinical deployment. Integrating additional clinical variables and expanding the training dataset with more negative cases may help improve the diagnostic performance and improve generalizability.  
The development of a web-based diagnostic interface further supports the clinical translation of this technology, enabling rapid, accessible, and standardized evaluation of radiographs. Future research should focus on prospective validation in real-world scenarios, exploration of multimodal models, and assessment of clinical impact on decision making and patient outcomes.  
Overall, AI-based tools offer a promising avenue to enhance diagnostic accuracy, reduce interobserver variability, and facilitate timely management of diabetic foot infections. While they are not intended to replace expert clinical judgment, these systems may play a valuable role in supporting clinicians across diverse healthcare settings, ultimately contributing to better patient care and reduced complication rates.  
Conceptualization, F.J.A.-A., A.T.-G. and J.L.L.-M.; methodology, F.J.A.-A., A.T.-G. and I.S.-C.; software, F.J.A.-A.; validation, F.J.A.-A., A.T.-G. and J.L.L.-M.; formal analysis, F.J.A.-A.; investigation, F.J.A.-A., A.T.-G. and J.L.L.-M.; resources, F.J.A.-A. and A.T.-G.; data curation, F.J.A.-A.; writing--original draft preparation, F.J.A.-A., A.T.-G. and J.L.L.-M.; writing--review and editing, F.J.A.-A., A.T.-G., [PERSON]-M., [PERSON]-C., E.G.-M. and J.L.L.-M.; visualization, M.L.-M., [PERSON]-C. and E.G.-M.; supervision, J.L.L.-M.; project administration, F.J.A.-A. and J.L.L.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted in accordance with the Declaration of Helsinki. The corresponding institutional review board granted ethical approval, which ensured compliance with data protection regulations (24/738-E, approval date: 19 November 2024).  
Patient consent was waived due to the retrospective nature of this study.  
The data are not publicly available due to privacy or ethical restrictions.  
During the preparation of this manuscript, the authors used ChatGPT (GPT-4.5, OpenAI) for assistance with Python code implementation, debugging, and model deployment within the web interface. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial intelligence \\ CNNs & Deep convolutional neural networks \\ DFO & Diabetic foot osteomyelitis \\ DFU & Diabetic foot ulcer \\ DL & Deep learning \\ MRI & Magnetic resonance imaging \\ NPV & Negative predictive value \\ PET-CT & Positron emission tomography-computed tomography \\ PPV & Positive predictive value \\ PTB & Probe-to-bone \\ \end{tabular}