Development of an Automatic Ultrasound Image Classification System for Pressure Injury Based on Deep Learning  
[PERSON]  
1 Department of Imaging Nursing Science, Graduate School of Medicine, The University of Tokyo, Tokyo 1130033, Japan; [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (N.T.); [EMAIL_ADDRESS] (Y.M.)  
[PERSON]  
2 Imaging Technology Center, Fujifilim Corporation, Tokyo 1070052, Japan; [EMAIL_ADDRESS] 2  
[PERSON]  
3 Department of Gerontological Nursing/Wound Care Management, Graduate School of Medicine, The University of Tokyo, Tokyo 1130033, Japan; [EMAIL_ADDRESS] (G.N.); ayakitamura@gec.  
Pressure injuries are commonly characterized by skin deterioration, resulting in an open wound. Moreover, as most pressure injuries may be attributed to excessive friction and moisture on the skin surface, some of them often begin as \"deep tissue pressure injuries\" (DTPIs) that start deep below the skin surface at the bone-muscle interface [9]. DTPIs cause rapid deterioration of the skin. Therefore, in many cases, it is not determined that a patient has a DTPI until it worsens, and early detection, treatment, and care are extremely important.  
Recently, various bedside technologies, including ultrasound (US) and subepidermal moisture measurement, have been used for the early detection of DTPIs. Although subepidermal moisture measurement is an extremely simple and useful method for the early detection of DTPIs by assessing skin physiological function [10; 11], it cannot actually visualize deep tissues and continuously monitor their morphological condition. Conversely, US is a promising tool that allows noninvasive assessment of deep tissues for the early detection of DTPIs [12; 13; 14; 15; 16]. US is often available at the bedside, owing to recent advancements in image quality and portability, and a classification algorithm using US images for pressure injuries has been developed [17]. This algorithm suggests distinguishing the types based on four US findings: unclear layer structure, hypoechoic/anechoic lesions, boundary of hypoechoic/anechoic lesions, and pattern of hypoechoic/anechoic lesions (cloud-like pattern or cobblestone-like pattern). Since US findings can visualize the condition of deep tissue (unclear layer structure: slight edema; cobblestone-like pattern: strong edema; cloud-like pattern: necrotic tissue; anechoic pattern: liquid storage), they allow the selection of adequate wound treatment and care.  
However, the classification of US images of pressure injury depends on the operator's skill in image interpretation, and US for pressure injury is a procedure that can only be performed by a limited number of highly trained medical professionals. It is difficult to distinguish between cobblestone- and cloud-like patterns. The cobblestone-like pattern is a finding indicating severe edema, while the cloud-like pattern is one of the findings of DTPIs that indicates the presence of necrotic tissue [18], which can cause deterioration; therefore, it is necessary to distinguish them accurately. Therefore, the authors devised a support system to automatically classify US images of pressure injuries. Recently, deep learning techniques, commonly known as artificial intelligence (AI) functions or techniques, have been employed to automatically identify the wound area [19]. Although some studies have used machine learning to classify US images of the skin and soft tissue [20; 21; 22; 23; 24], no study has focused on US findings of pressure injuries or wound sites.  
Therefore, this study aimed to develop an automatic US image classification system for pressure injury based on deep learning. This system enables the assessment of DTPIs using US by non-specialists who do not have a high skill in image interpretation. As a first step, the development was carried out using a deep learning-based segmentation method based on a U-Net convolutional neural network to extract ultrasound findings from ultrasound images. The evaluation was performed by calculating the detection rate of the new system against expert annotations.