TY - GEN
T1 - Identification of Alterations in Surgical Wounds Through the Application of Artificial Intelligence in Digital Images
AU - Rodiguez Prada, Javier Armando
AU - Cote Florez, Alvaro Augusto
AU - Pineda Gomez, Amolfi Hernando
AU - Vargas Cardona, Hernan Dario
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/11/6
Y1 - 2024/11/6
N2 - Globally, surgical site infections are common complications that are both serious and costly. While telemedicine has enhanced the remote assessment of surgical wounds, it still faces limitations. This study introduces a convolutional neural network (CNN) model designed to automatically classify digital images of surgical wounds as either altered or unaltered. The study utilized a dataset of 4,262 segmented and expert-labeled images. The CNN model achieved an accuracy of 83.46%, a sensitivity of 81.54%, and an AUROC of 92.22%. Although the MobileNet model demonstrated acceptable performance, it was less effective in comparison. The findings s uggest t hat C NNs a re e ffective for classifying images of surgical wounds, with potential for further improvement using advanced techniques and a multidisciplinary expert panel.
AB - Globally, surgical site infections are common complications that are both serious and costly. While telemedicine has enhanced the remote assessment of surgical wounds, it still faces limitations. This study introduces a convolutional neural network (CNN) model designed to automatically classify digital images of surgical wounds as either altered or unaltered. The study utilized a dataset of 4,262 segmented and expert-labeled images. The CNN model achieved an accuracy of 83.46%, a sensitivity of 81.54%, and an AUROC of 92.22%. Although the MobileNet model demonstrated acceptable performance, it was less effective in comparison. The findings s uggest t hat C NNs a re e ffective for classifying images of surgical wounds, with potential for further improvement using advanced techniques and a multidisciplinary expert panel.
KW - Classification
KW - Convolutional Neural Networks
KW - Segmentation
KW - Surgical Wounds
KW - Transfer Learning
UR - https://www.scopus.com/pages/publications/85215320064
UR - https://www.mendeley.com/catalogue/2d55f611-7d8a-330f-b7f5-5d29a43fda6e/
U2 - 10.1109/ciibbi63846.2024.10784973
DO - 10.1109/ciibbi63846.2024.10784973
M3 - Conference contribution
AN - SCOPUS:85215320064
SN - 9798331532352
T3 - 2024 3rd International Congress of Biomedical Engineering and Bioengineering, CIIBBI 2024
SP - 1
EP - 5
BT - 2024 3rd International Congress of Biomedical Engineering and Bioengineering, CIIBBI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Congress of Biomedical Engineering and Bioengineering, CIIBBI 2024
Y2 - 6 November 2024 through 8 November 2024
ER -