High-frequency ultrasound imaging in wound assessment: current perspectives

Non-invasive imaging modalities for wound assessment have become increasingly popular over the past two decades. The wounds can be developed superficially or from within deep tissues, depending on the nature of the dominant risk factors. Developing a reproducible quantitative method to assess wound-healing status has demonstrated to be a convoluted task. Advances in High-Frequency Ultrasound (HFU) skin scanners have expanded their application as they are cost-effective and reproducible diagnostic tools in dermatology, including for the measurement of skin thickness, the assessment of skin tumours, the estimation of the volume of melanoma and non-melanoma skin cancers, the visualisation of skin structure and the monitoring of the healing of acute and chronic wounds. Previous studies have revealed that HFU images carry dominant parameters and depict the phenomena occurring within deep tissue layers during the wound-healing process. However, the investigations have mostly focussed on the validation of HFU images, and few studies have utilised HFU imaging in quantitative assessment of wound generation and healing. This paper is an introductory review of the important studies proposed by the researchers in the context of wound assessment. The principles of dermasonography are briefly explained, followed by a review of the relevant literature that investigated the wound-healing process and tissue structures within the wound using HFU imaging

Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application