Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progress of the healing. While invasive methods to retrieve information are not only painful to the patients but may also increase the risk of infections, non-invasive techniques by means of imaging systems provide a better monitoring of the wound healing processes without causing any harm to the patients. These systems should include an accurate segmentation of the wound, the classification of its tissue types, the metrics including the diameter, area and volume, as well as the healing evaluation. Therefore, the aim of this survey is to provide the reader with an overview of imaging techniques for the analysis and monitoring of pressure injuries as an aid to their diagnosis, and proof of the efficiency of Deep Learning to overcome this problem and even outperform the previous methods. In this paper, 114 out of 199 papers retrieved from 8 databases have been analyzed, including also contributions on chronic wounds and skin lesions.

压伤在许多国家代表着巨大的医疗保健挑战。老年人和残疾人受这种快速增长的疾病影响最大。因此，对压力损伤的准确诊断对于有效治疗至关重要。这些伤口的特征是愈合过程的关键指标。尽管侵入式检索信息的方法不仅会使患者痛苦，而且可能增加感染的风险，但是借助成像系统的非侵入性技术可以更好地监控伤口的愈合过程，而不会对患者造成任何伤害。这些系统应包括伤口的精确分割，组织类型的分类，包括直径，面积和体积的指标以及愈合评估。因此，这项调查的目的是为读者提供用于分析和监测压力损伤以帮助其诊断的成像技术概述，并证明深度学习克服此问题甚至超越以前方法的效率。本文对从8个数据库中检索到的199篇论文中的114篇进行了分析，包括对慢性伤口和皮肤病变的贡献。