## 1 Introduction
Given these premises, the need clearly emerges to introduce and promote the use of technologies capable of automatically and accurately providing information regarding skin  
Figure 1: (**a**) The red outline describes a manual measurement of a generic wound, while the green outline describes the automatic segmentation performed by the device. The area of the rectangle and the area of the wound can have a difference of up to 30%. (**b**) A device for the acquisition, automatic segmentation, and area calculation of a skin lesion. Adapted from [7]. ulcers, thereby offering a support tool for physicians to quickly provide a comprehensive clinical picture of the patient and the status of the wound. As the healthcare landscape continues to evolve, the integration of AI and digital tools is revolutionizing traditional practices, setting new standards for efficiency and effectiveness in all the phases of the clinical practice [12]. This shift highlights the limitations of manual techniques and underscores the critical role of technology in advancing patient care. In recent years, there has been increasing attention to this topic and the development of new solutions for skin ulcer assessment that are more accurate and non-invasive. To this end, a study was conducted by [PERSON] et al. [13] to test a smartphone application capable of calculating the wound area from a picture. That method allowed for a more precise measurement by manually outlining the irregular shape of the lesion. However, it still heavily depended on the operator's experience and remained a time-consuming process. An innovative aspect in the evaluation of chronic wounds is the development of new approaches to standardize the accuracy of their measurement. For this reason, there is a push towards various computer-aided solutions for the automatic segmentation of skin ulcers, enabling more accurate and faster analysis [14]. Furthermore, several studies have demonstrated that protocols enabling easy and continuous monitoring through standardized data are key factors in preventing complications like infection and the occurrence of necrotic tissue, which can lead to the need for surgical intervention [15; 16]. This topic has been studied by various research groups, which have developed artificial intelligence (AI) algorithms demonstrating the reliability of such tools in the field of wound care [17]. Recent works [18; 19] have also demonstrated the capability of automatically identifying and determining the etiology of wounds through advanced computational means. In particular, an approach was proposed by [PERSON] et al. [20], who developed a technology, shown in Figure 1b, integrating a memristive Discrete-Time Cellular Neural Network (DT-CNN) capable of automatically identifying, classifying, and measuring wounds through 2D wound images, with an accuracy of up to 90%. In another work, the actual reliability of such a telemedical device for supporting the diagnosis and monitoring of skin ulcers was demonstrated through a clinical trial that showed the clinical utility and effectiveness of an AI-based tool capable of providing precise and reliable clinical information to the physician in a telemedical setting [21]. Given the current state of the art, the purpose of this work was to integrate a three-dimensional approach into the traditional two-dimensional methods for extracting wound assessment parameters, providing a more comprehensive picture of the skin lesion condition. Specifically, the goal was to develop an AI-powered tool for more accurate and efficient monitoring of the wound progression to support the physician, enabling the XR interaction and visualization of the patient's clinical history alongside the wound's two-dimensional and three-dimensional parameters, such as its area and perimeter. Such a tool provides the opportunity to support clinicians through automated and standardized processes, leading to more efficient patient management by enabling a rapid and accurate storage of clinical data.