Development of 3D-printed in-sole hydrogel-based sensor for early diagnosis of diabetic foot ulcers

糖尿病是一个快速增长的公共卫生问题，国际糖尿病联合会估计，到2040年，全球将有6.42亿人受到糖尿病的影响。糖尿病足溃疡(DFU)是糖尿病的主要和常见并发症，可导致长时间住院。DFU是一个普遍和严重的全球卫生问题，需要大量使用卫生资源，包括工作人员和治疗费用。对于老年人来说，DFU和相关并发症(如截肢)的风险更大，据报道，65岁及以上的人接受了一半的截肢手术。如果在早期阶段诊断或治疗，DFU是可以预防的。足底温度和压力被认为在DFU的发展中起着重要作用，监测这些物理参数可以帮助DFU的早期诊断，减少与健康相关的并发症和治疗成本。本项目旨在开发一种新型的集成鞋垫水凝胶传感器，以同时监测足底压力和温度。这项研究有可能通过监测患者足部与温度和压力相关的数据来早期诊断DFU，以避免或延迟足部溃疡和相关并发症。它在临床资源方面提供了巨大的节省，并可以显著改善全球数百万人的生活。它还将为人工神经网络的未来发展提供数据，该网络有可能用于预测建模，以支持临床决策。

Diabetes is a fast-growing public health issue, and the International Diabetes Federation estimated that 642 million will be affected by it by 2040 globally. Diabetic foot ulcers (DFUs) are known as major and common complications of diabetes, which can lead to a prolonged hospital stay. DFUs are a prevalent and serious global health issue that requires significant use of health resources, including staff and treatment costs. The risk of DFUs and associated complications such as amputation is greater for older people, where half of the amputations are reported to be performed for people aged 65 and older. DFUs are preventable if they are diagnosed or treated at early stages. Plantar temperature and pressure are believed to play an important role in DFUs development and monitoring these physical parameters can facilitate the early diagnosis of DFUs, reduce health-related complications and treatment costs. This project aims to develop a novel integrated insole hydrogel-based sensor to simultaneously monitor the plantar pressure and temperature. This research has the potential to diagnose DFUs at an early stage by monitoring the patients' feet data related to the temperature and pressure in order to avoid or delay foot ulcers and associated complications. It offers huge savings in terms of clinical resources and can significantly improve the lives of millions of people worldwide. It will also provide data for the future development of an artificial neural network, with the potential to be used in predictive modelling to support clinical decisions.