Kaselimi Maria, Protopapadakis Eftychios, Doulamis Anastasios, Doulamis Nikolaos
National Technical University of Athens, School of Rural, Surveying and Geoinformatics Engineering, Athens, Greece.
Front Physiol. 2022 Oct 21;13:924546. doi: 10.3389/fphys.2022.924546. eCollection 2022.
Diabetic foot complications have multiple adverse effects in a person's quality of life. Yet, efficient monitoring schemes can mitigate or postpone any disorders, mainly by early detecting regions of interest. Nowadays, optical sensors and artificial intelligence (AI) tools can contribute efficiently to such monitoring processes. In this work, we provide information on the adopted imaging schemes and related optical sensors on this topic. The analysis considers both the physiology of the patients and the characteristics of the sensors. Currently, there are multiple approaches considering both visible and infrared bands (multiple ranges), most of them coupled with various AI tools. The source of the data (sensor type) can support different monitoring strategies and imposes restrictions on the AI tools that should be used with. This review provides a comprehensive literature review of AI-assisted DFU monitoring methods. The paper presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and the challenges for transferring these methods into a practical and trustworthy framework for sufficient remote management of the patients.
糖尿病足并发症对人的生活质量有多种不利影响。然而，有效的监测方案可以减轻或推迟任何疾病，主要是通过早期检测感兴趣区域。如今，光学传感器和人工智能（AI）工具可以有效地促进此类监测过程。在这项工作中，我们提供了关于该主题所采用的成像方案和相关光学传感器的信息。分析考虑了患者的生理状况和传感器的特性。目前，有多种方法考虑可见光和红外波段（多个范围），其中大多数与各种人工智能工具相结合。数据来源（传感器类型）可以支持不同的监测策略，并对与之配合使用的人工智能工具施加限制。本综述对人工智能辅助的糖尿病足溃疡（DFU）监测方法进行了全面的文献综述。本文展示了大量最近发表的学术文章的成果。此外，本文还讨论了这些方法的亮点以及将这些方法转化为一个实用且可靠的框架以对患者进行充分远程管理所面临的挑战。
