AI-powered image acquisition and characterisation of dressings for patient-centred wound management

Wound dressings and their proper management are crucial to the wound's recovery. This process can be time-consuming and requires special knowledge to be effective. In order to improve the monitorization and decision-making processes, this work proposes a framework based on state-of-the-art Deep Learning models for automating the acquisition and analysis of wound dressings. Its development was supported by a novel dataset of dressing images annotated by experts regarding dressing state and regions of interest. The two-step acquisition pipeline resorts to a RetinaNet model to detect the dressing region, along with a reference marker, further used by the image validation module to ensure that the images fulfil the clinical adequacy requirements, such as the presence of a minimum periwound area. On top of its robust detection performance, its mobile deployment demonstrated its ability to support and standardise the image acquisition task. The characterisation module analyses the dressing areas provided by the detection model, using a MobileNetV3Small model to classify if the dressing is usable or in need of change and achieving an F1 score of 0.778. Thus, this work provides a promising system to streamline the dressing monitoring process, constituting an important advancement in this field with the potential to empower caregivers and healthcare professionals.