A Granulation Tissue Detection Model to Track Chronic Wound Healing in DM Foot Ulcers

ABSTRACT

Diabetes mellitus (DM) foot ulcer is a chronic wound and is highly related to the mortality and morbidity of infection, and might induce sepsis and foot amputation, especially during the isolation stage of the COVID-19 pandemic. Visual observation when changing dressings is the most common and traditional method of detecting wound healing. The formation of granulation tissues plays an important role in wound healing. In the complex pathophysiology of excess and unhealthy granulation induced by infection, oxygen supply may explain the wound healing process in DM patients with multiple complicated wounds. Thus, advanced and useful tools to observe the condition of wound healing are very important for DM patients with extremities ulcers. For this purpose, we developed an artificial intelligence (AI) detection model to identify the growth of granulation tissue of the wound bed. We recruited 100 patients to provide 219 images of wounds at different healing stages from 2 hospitals. This was performed to understand the wound images of inconsistent size, and to allow self-inspection on mobile devices, having limited computing resources. We segmented those images into 32 ×32 blocks and used a reduced ResNet-18 model to test them individually. Furthermore, we conducted a learning method of active learning to improve the efficiency of model training. Experimental results reveal that our model can identify the region of granulation tissue with an Intersection-over-Union (IOU) rate higher than 0.5 compared to the ground truth. Multiple cross-repetitive validations also confirm that the detection results of our model may serve as an auxiliary indicator for assessing the progress of wound healing. The preliminary findings may help to identify the granulation tissue of patients with DM foot ulcer, which may lead to better long-term home care during the COVID-19 pandemic. The current limit of our model is an IOU of about 0.6. If more actual data are available, the IOU is expected to improve. We can continue to use the currently established active learning process for subsequent training.