Computers in biology and medicine · 2025
Accessible healing phase classification of diabetic foot ulcer.
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Basiri Reza, de Mestral Charles, Popovic Milos R, Khan Shehroz S
Аннотация
OBJECTIVE: Diabetic foot ulcers (DFU) are complex wounds that, without proper treatment, can lead to leg amputation. Treatment of a DFU is multifaceted and requires a high level of clinical expertise. This study aims to inform medical triage and improve healing rates by developing accessible and automated assessment and classification of wound healing phases and analyzing and identifying essential clinical and wound features for the classification.
APPROACH: Machine learning models were evaluated using the Zivot DFU dataset to classify patients' wound healing phases from inflammation, proliferation, and remodeling classes. The models were trained on clinical features from 268 unique patients and 890 data points, including 80 features describing patient demographics, comorbidities, and wound characteristics. The area under the receiver operating characteristic curve, accuracy, and F1 values were used to assess the models' performances, and Shapley Additive Explanations were used to analyze the importance of features.
RESULTS: 56 of the 80 features provided an accuracy of 65 %, while 22 essential features were enough to achieve a lower but statistically similar accuracy with an ordinal three-class classification using random forest classifiers.
INNOVATION: This study provides a novel approach to classifying the wound-healing phase of a DFU based on key features available from wound- and patient-level metadata.
CONCLUSION: The accessible and automated machine learning approach's ease of use and reliability will promote early and continuous autonomous medical triaging, ultimately improving patient outcomes. Additionally, the identified essential clinical features correlating with healing phases provide insight into DFU data management.
APPROACH: Machine learning models were evaluated using the Zivot DFU dataset to classify patients' wound healing phases from inflammation, proliferation, and remodeling classes. The models were trained on clinical features from 268 unique patients and 890 data points, including 80 features describing patient demographics, comorbidities, and wound characteristics. The area under the receiver operating characteristic curve, accuracy, and F1 values were used to assess the models' performances, and Shapley Additive Explanations were used to analyze the importance of features.
RESULTS: 56 of the 80 features provided an accuracy of 65 %, while 22 essential features were enough to achieve a lower but statistically similar accuracy with an ordinal three-class classification using random forest classifiers.
INNOVATION: This study provides a novel approach to classifying the wound-healing phase of a DFU based on key features available from wound- and patient-level metadata.
CONCLUSION: The accessible and automated machine learning approach's ease of use and reliability will promote early and continuous autonomous medical triaging, ultimately improving patient outcomes. Additionally, the identified essential clinical features correlating with healing phases provide insight into DFU data management.