Enhancing Post-Surgical Wound Care in Anorectal Diseases: A Comparative Study of Advanced Convolutional Neural Network (CNN) Architectures for Image Classification and Analysis.

Journal: Annali italiani di chirurgia
PMID: 39723502

Abstract

AIM: Anorectal diseases, often requiring surgical intervention and careful post-operative wound management, pose substantial challenges in healthcare. This study presents a novel application of artificial intelligence, specifically machine learning, aimed at improving the classification and analysis of post-surgical wound images. By doing so, it seeks to enhance patient outcomes through personalized and optimized wound care strategies.

Authors

  • Qiaolan Zhang
    Department of Anorectal Surgery, Wuhan Tongji Aerospace City Hospital, 430416 Wuhan, Hubei, China.
  • Zhaobo Chen
    Department of Pharmacy, No.1 People's Hospital of Danjiangkou, 442700 Danjiangkou, Hubei, China.
  • Shunfang Hu
    Department of Anorectal, Deyang Jingyang District Hospital of Traditional Chinese Medicine, 618000 Deyang, Sichuan, China.
  • Xinkun Bao
    Department of Colorectal Surgery, Hubei Provincial Hospital of Traditional Chinese Medicine Affiliated to Hubei University of Chinese Medicine, 430071 Wuhan, Hubei, China.

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