CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy.
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy.
Biosensors (Basel). 2022 Nov 8;12(11):985. doi: 10.3390/bios12110985.
Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.
糖尿病足综合征是一种多因素病理，至少有三个主要病因因素，即周围神经病变、周围动脉疾病和感染。除了复杂性之外，糖尿病足综合征的另一个显著特征是其隐匿性，因为它经常缺乏早期症状。近年来，人们已经清楚地认识到糖尿病足综合征的患病率正在增加，而且它是对患者生活质量影响更大的糖尿病并发症之一。考虑到这种综合征的复杂性，人工智能 (AI) 方法似乎足以解决一些问题，例如及时筛查足部溃疡（甚至更糟的是截肢）的风险，这是基于适当的传感器技术。在这篇综述中，我们总结了该领域相关研究的主要发现，既关注基于 AI 的方法学方面，也关注主要的生理/临床研究结果。分析表明，将 AI 应用于不同技术获得的数据提供了有希望的结果，但我们认为，未来的研究可能受益于纳入基于简单传感器的定量测量，而这些传感器仍未得到充分利用。
