Smart Percussion for automated diagnostics

这项研究的目的是开发一种智能敲击设备，帮助医生更有效地诊断病人，减少对有害扫描和创伤性干预的需要。该设备可以部署在初级和二级医疗之外。该设备开发的关键要素是执行打击实验的幻影和两种不同的行为建模方法：通过解决声音传播控制方程开发的基于物理的模型，以及通过使用幻影观察训练分类器开发的基于机器学习的模型。该项目属于EPSRC的数字经济和医疗保健技术主题和医疗保健技术重大挑战。它将通过支持新设备的开发来提高诊断效率，最大限度地降低成本，同时降低患者的风险，从而直接做出贡献。

The aim of this research is to develop a smart percussion device to help medical practitioners diagnose patients more efficiently, reducing the need for harmful scans and traumatising interventions. The device could be deployed outside of primary and secondary care.The key elements in the development of this device are a phantom on which to perform percussion experiments and two different approaches to modelling the behaviour: a physics-based model developed by solving the governing equation for sound propagation, and a machine-learning based model developed by training a classifier using phantom observations.The project falls under Digital Economies and Healthcare Technologies themes of EPSRC and under Healthcare Technologies Grand Challenges. It would contribute directly by supporting the development of novel devices to enhance the efficacy of diagnosis, minimising costs while reducing risk to patients.