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Abstract
Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing a significant risk to diabetes patients, including the potential for amputations, highlighting the urgent need for improved diagnostic tools and care standards in the field. To address this issue, the objective of this study was to develop and evaluate the Smart Diabetic Foot Ulcer Scoring System, ScoreDFUNet, which incorporates artificial intelligence (AI) and image analysis techniques, aiming to enhance the precision and consistency of diabetic foot ulcer assessment. ScoreDFUNet demonstrates precise categorization of DFU images into “ulcer,” “infection,” “normal,” and “gangrene” areas, achieving a noteworthy accuracy rate of 95.34% on the test set, with elevated levels of precision, recall, and F1 scores. Comparative evaluations with dermatologists affirm that our algorithm consistently surpasses the performance of junior and mid-level dermatologists, closely matching the assessments of senior dermatologists, and rigorous analyses including Bland–Altman plots and significance testing validate the robustness and reliability of our algorithm. This innovative AI system presents a valuable tool for healthcare professionals and can significantly improve the care standards in the field of diabetic foot ulcer assessment.
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Details
1 Hunan First Normal University, School of Computer Science, Changsha, China (GRID:grid.448863.5) (ISNI:0000 0004 1759 9902); Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Department of Dermatology, Shenzhen People’s Hospital, The Second Clinical Medical College, Shenzhen, China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790)
2 Hunan First Normal University, School of Computer Science, Changsha, China (GRID:grid.448863.5) (ISNI:0000 0004 1759 9902)
3 Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Department of Dermatology, Shenzhen People’s Hospital, The Second Clinical Medical College, Shenzhen, China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790); Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, China (GRID:grid.263817.9)
4 Central South University, Department of Clinical Nursing, Xiangya Hospital, Changsha, China (GRID:grid.216417.7) (ISNI:0000 0001 0379 7164); Central South University, Foot Prevention and Treatment Center, Xiangya Hospital, Changsha, China (GRID:grid.216417.7) (ISNI:0000 0001 0379 7164)
5 Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Department of Dermatology, Shenzhen People’s Hospital, The Second Clinical Medical College, Shenzhen, China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790); Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, China (GRID:grid.263817.9); The First Affiliated Hospital, Southern University of Science and Technology, Department of Geriatrics, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790)