Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation
Objective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). Research Design and Methods: This retrospective study included 168 patients with type one or type two diabetes an...
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MDPI AG
2025-08-01
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| author | Francisco Javier Álvaro-Afonso Aroa Tardáguila-García Mateo López-Moral Irene Sanz-Corbalán Esther García-Morales José Luis Lázaro-Martínez |
| author_facet | Francisco Javier Álvaro-Afonso Aroa Tardáguila-García Mateo López-Moral Irene Sanz-Corbalán Esther García-Morales José Luis Lázaro-Martínez |
| author_sort | Francisco Javier Álvaro-Afonso |
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| description | Objective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). Research Design and Methods: This retrospective study included 168 patients with type one or type two diabetes and clinical suspicion of DFO confirmed via a surgical bone biopsy. An experienced clinician and a pretrained ResNet-50 model independently interpreted the radiographs. The model was developed using Python-based frameworks with ChatGPT assistance for coding. The diagnostic performance was assessed against the histopathological findings, calculating sensitivity, specificity, the positive predictive value (PPV), the negative predictive value (NPV), and the likelihood ratios. Agreement between the AI model and the clinician was evaluated using Cohen’s kappa coefficient. Results: The AI model demonstrated high sensitivity (92.8%) and PPV (0.97), but low-level specificity (4.4%). The clinician showed 90.2% sensitivity and 37.8% specificity. The Cohen’s kappa coefficient between the AI model and the clinician was −0.105 (<i>p</i> = 0.117), indicating weak agreement. Both the methods tended to classify many cases as DFO-positive, with 81.5% agreement in the positive cases. Conclusions: This study demonstrates the potential of IA to support the radiographic diagnosis of DFO using a ResNet-50-based deep learning model. AI-assisted radiographic interpretation could enhance early DFO detection, particularly in high-prevalence settings. However, further validation is necessary to improve its specificity and assess its utility in primary care. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-15ac10d6b69d4b05a78a30d5b46d23e52025-08-20T03:36:31ZengMDPI AGApplied Sciences2076-34172025-08-011515858310.3390/app15158583Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph InterpretationFrancisco Javier Álvaro-Afonso0Aroa Tardáguila-García1Mateo López-Moral2Irene Sanz-Corbalán3Esther García-Morales4José Luis Lázaro-Martínez5Diabetic Foot Unit, University Podiatric Clinic, Faculty of Nursing, Physiotherapy and Podiatry, Complutense University of Madrid, 28040 Madrid, SpainDiabetic Foot Unit, University Podiatric Clinic, Faculty of Nursing, Physiotherapy and Podiatry, Complutense University of Madrid, 28040 Madrid, SpainDiabetic Foot Unit, University Podiatric Clinic, Faculty of Nursing, Physiotherapy and Podiatry, Complutense University of Madrid, 28040 Madrid, SpainDiabetic Foot Unit, University Podiatric Clinic, Faculty of Nursing, Physiotherapy and Podiatry, Complutense University of Madrid, 28040 Madrid, SpainDiabetic Foot Unit, University Podiatric Clinic, Faculty of Nursing, Physiotherapy and Podiatry, Complutense University of Madrid, 28040 Madrid, SpainDiabetic Foot Unit, University Podiatric Clinic, Faculty of Nursing, Physiotherapy and Podiatry, Complutense University of Madrid, 28040 Madrid, SpainObjective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). Research Design and Methods: This retrospective study included 168 patients with type one or type two diabetes and clinical suspicion of DFO confirmed via a surgical bone biopsy. An experienced clinician and a pretrained ResNet-50 model independently interpreted the radiographs. The model was developed using Python-based frameworks with ChatGPT assistance for coding. The diagnostic performance was assessed against the histopathological findings, calculating sensitivity, specificity, the positive predictive value (PPV), the negative predictive value (NPV), and the likelihood ratios. Agreement between the AI model and the clinician was evaluated using Cohen’s kappa coefficient. Results: The AI model demonstrated high sensitivity (92.8%) and PPV (0.97), but low-level specificity (4.4%). The clinician showed 90.2% sensitivity and 37.8% specificity. The Cohen’s kappa coefficient between the AI model and the clinician was −0.105 (<i>p</i> = 0.117), indicating weak agreement. Both the methods tended to classify many cases as DFO-positive, with 81.5% agreement in the positive cases. Conclusions: This study demonstrates the potential of IA to support the radiographic diagnosis of DFO using a ResNet-50-based deep learning model. AI-assisted radiographic interpretation could enhance early DFO detection, particularly in high-prevalence settings. However, further validation is necessary to improve its specificity and assess its utility in primary care.https://www.mdpi.com/2076-3417/15/15/8583artificial intelligencediabetic footdiabetic foot osteomyelitis |
| spellingShingle | Francisco Javier Álvaro-Afonso Aroa Tardáguila-García Mateo López-Moral Irene Sanz-Corbalán Esther García-Morales José Luis Lázaro-Martínez Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation Applied Sciences artificial intelligence diabetic foot diabetic foot osteomyelitis |
| title | Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation |
| title_full | Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation |
| title_fullStr | Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation |
| title_full_unstemmed | Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation |
| title_short | Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation |
| title_sort | using artificial intelligence for detecting diabetic foot osteomyelitis validation of deep learning model for plain radiograph interpretation |
| topic | artificial intelligence diabetic foot diabetic foot osteomyelitis |
| url | https://www.mdpi.com/2076-3417/15/15/8583 |
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