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Automatic Classification of Diabetic Foot Ulcer Images : A Transfer-Learning Approach to Detect Wound Maceration

  • 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.

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Metadaten
Author:Jens HüsersORCiD, Guido Hafer, Jan Heggemann, Stefan Wiemeyer, Mareike PrzysuchaORCiD, Joachim DissemondORCiD, Maurice MoellekenORCiD, Cornelia Erfurt-BergeORCiD, Ursula Hertha HübnerORCiD
Title (English):Automatic Classification of Diabetic Foot Ulcer Images : A Transfer-Learning Approach to Detect Wound Maceration
URN:urn:nbn:de:bsz:959-opus-35870
DOI:https://doi.org/10.3233/SHTI210919
ISBN:978-1-64368-250-1
ISBN:978-1-64368-251-8
Parent Title (English):Informatics and Technology in Clinical Care and Public Health
Publisher:IOS Press
Place of publication:Amsterdam, Berlin, Washington (DC)
Document Type:Conference Proceeding
Language:English
Year of Completion:2022
Release Date:2022/08/05
First Page:301
Last Page:304
Note:
International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), 16.10. - 17.10.2021, Athens (Greece)
Faculties:Fakultät WiSo
DDC classes:600 Technik, Medizin, angewandte Wissenschaften / 610 Medizin, Gesundheit
Review Status:Veröffentlichte Fassung/Verlagsversion
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International