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2026
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| Online Access: | https://doi.org/10.5281/zenodo.20079805 |
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| _version_ | 1866902170609123328 |
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| author | Emma Mastro Bilal Chaudhry Eddy Rios Christos Meimeteas Giorgio Giatsidis UMass Chan Medical School |
| author_facet | Emma Mastro Bilal Chaudhry Eddy Rios Christos Meimeteas Giorgio Giatsidis UMass Chan Medical School |
| contents | PURPOSE: Artificial intelligence is increasingly shaping plastic and reconstructive surgical decision-making. Wound assessment tasks, such as classification, measurement, and prediction, are particularly suited to AI automation. However, the diagnostic accuracy and clinical generalizability of image-based models remain uncertain. This systematic review evaluated the performance of AI models for chronic wound classification. METHODS: A systematic search of PubMed, Scopus, and IEEE Xplore (October 1, 2024) identified 3,537 records. After removing duplicates, 3,112 articles were screened. Eligible studies applied image-based AI to human chronic wounds (diabetic, venous, arterial, or pressure ulcers) and compared outputs with a clinical reference standard. A sub-analysis of 33 models focusing on surgical decision-making for condition classification, extracted for accuracy, AUC, sensitivity, and specificity for infection, ischemia, necrosis, gangrene, and staging tasks. RESULTS: The majority of studies (91%) reported quantitative performance. Accuracies ranged from 50-99.6% for infection and 55-99% for ischemia, with more variable results for necrosis (70-85%). Notably, 27 of 33 studies (82%) utilized the Diabetic Foot Ulcer (DFU2020) dataset or derivatives. Models trained and tested on these datasets achieved the highest accuracies (≥95%), whereas those evaluated on independent hospital images showed more moderate, clinically realistic results (65-86%). Patch-based testing predominated (81%), in which wound images were divided into small tiles to increase sample size. Though a common Computer Vision strategy, it can artificially inflate accuracy between patches from the same wound. Three recent studies introduced transformer-based or hybrid transformer-Convoluted Neural Network (CNN) architectures (DeiT, SimpleViT, and SwinDFU-Net). These models can capture spatial relationships rather than focusing on localized features like conventional CNNs (ResNet or EfficientNet). However, due to limited adoption, their reported accuracies varied substantially (50-96%). CONCLUSION: AI models for wound condition classification demonstrate internal accuracy when evaluated on standardized DFU datasets but exhibit reduced performance on independent clinical images. Heavy reliance on a single dataset family and patch-based validation likely overestimates diagnostic capability by reusing limited image pools and non-independent data. Future research should prioritize enriching the quantity and quality of patient wound datasets to ensure clinical translatability. *Source: https://ps-rc.org/meeting/Program/2026/EP117.cgi* |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20079805 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
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| spellingShingle | Systematic Review Of AI Models For Chronic Wound Condition Classification Emma Mastro Bilal Chaudhry Eddy Rios Christos Meimeteas Giorgio Giatsidis UMass Chan Medical School plastic surgery reconstructive surgery conference abstract PSRC 2026 PURPOSE: Artificial intelligence is increasingly shaping plastic and reconstructive surgical decision-making. Wound assessment tasks, such as classification, measurement, and prediction, are particularly suited to AI automation. However, the diagnostic accuracy and clinical generalizability of image-based models remain uncertain. This systematic review evaluated the performance of AI models for chronic wound classification. METHODS: A systematic search of PubMed, Scopus, and IEEE Xplore (October 1, 2024) identified 3,537 records. After removing duplicates, 3,112 articles were screened. Eligible studies applied image-based AI to human chronic wounds (diabetic, venous, arterial, or pressure ulcers) and compared outputs with a clinical reference standard. A sub-analysis of 33 models focusing on surgical decision-making for condition classification, extracted for accuracy, AUC, sensitivity, and specificity for infection, ischemia, necrosis, gangrene, and staging tasks. RESULTS: The majority of studies (91%) reported quantitative performance. Accuracies ranged from 50-99.6% for infection and 55-99% for ischemia, with more variable results for necrosis (70-85%). Notably, 27 of 33 studies (82%) utilized the Diabetic Foot Ulcer (DFU2020) dataset or derivatives. Models trained and tested on these datasets achieved the highest accuracies (≥95%), whereas those evaluated on independent hospital images showed more moderate, clinically realistic results (65-86%). Patch-based testing predominated (81%), in which wound images were divided into small tiles to increase sample size. Though a common Computer Vision strategy, it can artificially inflate accuracy between patches from the same wound. Three recent studies introduced transformer-based or hybrid transformer-Convoluted Neural Network (CNN) architectures (DeiT, SimpleViT, and SwinDFU-Net). These models can capture spatial relationships rather than focusing on localized features like conventional CNNs (ResNet or EfficientNet). However, due to limited adoption, their reported accuracies varied substantially (50-96%). CONCLUSION: AI models for wound condition classification demonstrate internal accuracy when evaluated on standardized DFU datasets but exhibit reduced performance on independent clinical images. Heavy reliance on a single dataset family and patch-based validation likely overestimates diagnostic capability by reusing limited image pools and non-independent data. Future research should prioritize enriching the quantity and quality of patient wound datasets to ensure clinical translatability. *Source: https://ps-rc.org/meeting/Program/2026/EP117.cgi* |
| title | Systematic Review Of AI Models For Chronic Wound Condition Classification |
| topic | plastic surgery reconstructive surgery conference abstract PSRC 2026 |
| url | https://doi.org/10.5281/zenodo.20079805 |