Secco Jacopo, Spinazzola Elisabetta, Pittarello Monica, Ricci Elia, Pareschi Fabio
Department of Electronics and Telecommunications, Politecnico di Torino, 10123, Torino, Italy.
Vulnology Unit, Clinica Eporediese, 10015, Ivrea, Italy.
Sci Rep. 2024 Dec 28;14(1):30839. doi: 10.1038/s41598-024-81521-9.
Chronic wounds are a syndrome that affects around 4% of the world population due to several pathologies. The COV-19 pandemic has enforced the need of developing new techniques and technologies that can help clinicians to monitor the affected patients easily and reliably. In this prospective observational study a new device, the Wound Viewer, that works through a memristor-based Discrete-Time Cellular Neural Network (DT-CNN) has been developed and tested through a clinical trial of 150 patients. The WV has been developed to serve as the state-of-art tool, capable to return the actual clinical information that is most needed by the caregivers: through the WBP scale, it classifies four classes of wounds by the type of tissue: A-only granular tissue; B-<50% slough; C->50% slough; D-necrosis. This work aims to describe in depth the technology and the computational techniques that have been implemented, and to demonstrate reliability in automatically identifying, classifying through internationally accepted clinical scales and measuring such wounds, that peaked to over a 90% of accuracy.
慢性伤口是一种由于多种病理状况影响着全球约4%人口的综合征。新冠疫情促使人们需要开发新的技术，以帮助临床医生轻松、可靠地监测受影响的患者。在这项前瞻性观察研究中，一种通过基于忆阻器的离散时间细胞神经网络（DT-CNN）工作的新设备——伤口观察仪（Wound Viewer）已被开发出来，并通过对150名患者的临床试验进行了测试。伤口观察仪的开发目的是作为一种先进工具，能够提供护理人员最需要的实际临床信息：通过伤口床准备（WBP）量表，它根据组织类型将伤口分为四类：A类——仅为颗粒组织；B类——腐肉占比<50%；C类——腐肉占比>50%；D类——坏死。这项工作旨在深入描述所实施的技术和计算技术，并证明其在通过国际认可的临床量表自动识别、分类和测量此类伤口方面的可靠性，准确率高达90%以上。
