@inproceedings{HuesersHaferHeggemannetal.2022,
  author    = {Jens H{\"u}sers and Guido Hafer and Jan Heggemann and Stefan Wiemeyer and Mareike Przysucha and Joachim Dissemond and Maurice Moelleken and Cornelia Erfurt-Berge and Ursula Hertha H{\"u}bner},
  title     = {Automatic Classification of Diabetic Foot Ulcer Images : A Transfer-Learning Approach to Detect Wound Maceration},
  series = {Informatics and Technology in Clinical Care and Public Health},
  publisher = {IOS Press},
  address   = {Amsterdam, Berlin, Washington (DC)},
  isbn      = {978-1-64368-250-1},
  doi       = {10.3233/SHTI210919},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:959-opus-35870},
  pages     = {301 -- 304},
  year      = {2022},
  abstract  = {Diabetic foot ulcer (DFU) is a chronic wound and a common diabetic complication as 2\% – 6\% of diabetic patients witness the onset thereof. The DFU can lead to severe health threats such as infection and lower leg amputations, Coordination of interdisciplinary wound care requires well-written but time-consuming wound documentation. Artificial intelligence (AI) systems lend themselves to be tested to extract information from wound images, e.g. maceration, to fill the wound documentation. A convolutional neural network was therefore trained on 326 augmented DFU images to distinguish macerated from unmacerated wounds. The system was validated on 108 unaugmented images. The classification system achieved a recall of 0.69 and a precision of 0.67. The overall accuracy was 0.69. The results show that AI systems can classify DFU images for macerations and that those systems could support clinicians with data entry. However, the validation statistics should be further improved for use in real clinical settings. In summary, this paper can contribute to the development of methods to automatic wound documentation.},
  language  = {en}
}