A novel approach for diabetic foot diagnosis: Deep learning-based detection of lower extremity arterial stenosis.

Journal: Diabetes research and clinical practice
PMID: 38049035

Abstract

PURPOSE OF THE STUDY: Assessing the lower extremity arterial stenosis scores (LEASS) in patients with diabetic foot ulcer (DFU) is a challenging task that requires considerable time and efforts from physicians, and it may yield varying results. The presence of vascular wall calcification and other irrelevant tissue information surrounding the vessel can further compound the difficulties of this evaluation. Automatic detection of lower extremity arterial stenosis (LEAS) is expected to help doctors develop treatment plans for patients faster.

Authors

  • Chongxin Wu
    School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Changpeng Xu
    Department of Orthopaedics, Guangdong Second Provincial General Hospital, Guangzhou 510317, China.
  • Shuanji Ou
    Department of Orthopaedics, Guangdong Second Provincial General Hospital, Guangzhou 510317, China.
  • Xiaodong Wu
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA.
  • Jing Guo
    College of Chemical Engineering, Department of Pharmaceutical Engineering, Northwest University, Xi'an, Shaanxi, China.
  • Yong Qi
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
  • Shuting Cai
    School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: shutingcai@gdut.edu.cn.

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