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

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.

Sistemas de apoio à decisão clínica em úlceras de pé diabético:revisão de escopo

“…14. Husers J, Hafer G, Heggemann J, Wiemeyer S, John SM, Hubner U (2022)/Alemanha/Studies in health technology and informatics (26) Desenvolver um esquema de estratificação que permita a classificação de pacientes com e sem risco de amputação maior.…”

“…Aprendizado de máquina/Método Bayesiano (26) Predizer o risco de amputações em pacientes com pé diabético, com base em características sociodemográficas e clínicas.…”

“…No entanto, as principais limitações apontadas devem-se à quantidade insuficiente de estudos com a população alvo, ou seja, pacientes propensos à ulceração e amputação, e/ou testagem limitada a ambientes comunitários com amostra restrita (9,15,24,25,31,33) , isso aliado à resistência dos profissionais da saúde quanto à incorporação da ferramenta na rotina de cuidados (15,16,(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(31)(32)(33)(34) e incompletude das informações disponíveis nos bancos de dados (5,15,18,19,32,33) .…”

A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification

“…[19], an ensemble CNN network was introduced for the binary classification of infection and ischemia in diabetic wounds. Husers et al [20] applied the transfer learning approach and presented a model based on Mobilenet-v1 to classify macerations in diabetic foot ulcers. The model attained an accuracy of 69% on the dataset having the 416 wound images collected from the wound care center of Christliches Klinikum Melle, Germany.…”

Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network

“…The logistic regression algorithm achieves an area under the curve value of 96.50%. Hüsers et al [ 17 ] presented the transfer learning method to detect the wound maceration and was able to achieve a recall of 0.69. Carlos Padierna et al [ 18 ] extracted features from infrared images of the upper side of the foot and toes to propose a classification approach for finding PAD and achieved 92.64% using SVM.…”