Smart diabetic foot ulcer scoring system

Wang，Zheng1,2; Tan，Xinyu1; Xue，Yang1; Xiao，Chen2,4; Yue，Kejuan1; Lin，Kaibin1; Wang，Chong2,4; Zhou，Qiuhong3,6; Zhang，Jianglin2,4,5

2024-12-01

10.1038/s41598-024-62076-1

Scientific Reports   影响因子和分区

2045-2322

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.

Biomedical image analysis Deep learning Diabetic foot ulcers Transfer learning

英语

通讯

2-s2.0-85193983974

Scopus

1.School of Computer Science,Hunan First Normal University,Changsha,410205,China
2.Department of Dermatology,Shenzhen People’s Hospital,The Second Clinical Medical College,Jinan University,The First Affiliated Hospital,Southern University of Science and Technology,Shenzhen,Guangdong,518020,China
3.Department of Clinical Nursing,Xiangya Hospital,Central South University,Changsha,410008,China
4.Candidate Branch of National Clinical Research Center for Skin Diseases,Shenzhen,Guangdong,518020,China
5.Department of Geriatrics,Shenzhen People’s Hospital,The Second Clinical Medical College,Jinan University,The First Affiliated Hospital,Southern University of Science and Technology,Shenzhen,Guangdong,518020,China
6.Foot Prevention and Treatment Center,Xiangya Hospital,Central South University,Changsha,410008,China

Wang，Zheng,Tan，Xinyu,Xue，Yang,et al. Smart diabetic foot ulcer scoring system[J]. Scientific Reports,2024,14(1).

Wang，Zheng.,Tan，Xinyu.,Xue，Yang.,Xiao，Chen.,Yue，Kejuan.,...&Zhang，Jianglin.(2024).Smart diabetic foot ulcer scoring system.Scientific Reports,14(1).

Wang，Zheng,et al."Smart diabetic foot ulcer scoring system".Scientific Reports 14.1(2024).