Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot.

Journal: Frontiers in endocrinology
PMID: 40270716

Abstract

OBJECTIVE: This study aims to conduct an in-depth analysis of diabetic foot ulcer (DFU) images using deep learning models, achieving automated segmentation and classification of the wounds, with the goal of exploring the application of artificial intelligence in the field of diabetic foot care.

Authors

  • Guang-Xin Zhou
    Department of Endocrinology, Air Force Medical Center, Air Force Medical University, Beijing, China.
  • Yu-Kun Tao
    Department of Endocrinology, Air Force Medical Center, Air Force Medical University, Beijing, China.
  • Jin-Zheng Hou
    Department of Endocrinology, Air Force Medical Center, Air Force Medical University, Beijing, China.
  • Hui-Juan Zhu
    Department of Endocrinology, Air Force Medical Center, Air Force Medical University, Beijing, China.
  • Li Xiao
    Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
  • Na Zhao
    Department of Gynecology, Peking University First Hospital Ningxia Women and Children's Hospital, Yinchuan, Ningxia, China.
  • Xiao-Wen Wang
    Chongqing Zhijian Life Technology Co., Ltd, Chongqing, China.
  • Bao-Lin Du
    Chongqing Zhijian Life Technology Co., Ltd, Chongqing, China.
  • Da Zhang
    Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA.

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