Window into the Mind: Handheld Spectroscopic Eye-safe Device (EyeD) for Neurodiagnostics

创伤性脑损伤（TBI）是全球发病率和死亡率的主要原因，并发症发生率高，需要长期护理，造成长期创伤后神经系统疾病，并且在英国每年的社会经济成本为75亿英镑。虽然必须迅速做出影响治疗的关键决定，但众所周知，TBI在住院前很难诊断，有时会导致不正确的患者管理。及时评估损伤严重程度是TBI患者正确治疗的优先事项。然而，目前的技术支持不足，无法满足诊断需求，表现出灵敏度低，特殊处理要求和复杂，昂贵的程序。提出了一种非侵入性便携式技术来诊断和监测TBI和神经退行性疾病，通过测量眼后部可见的视神经变化。视神经，沐浴在脑脊液中，与中枢神经系统相连，构成了一个光学上清晰的“大脑窗口”。其目的是开发一种便携式技术，以检测脑损伤时脑脊液中的生化变化，使用专门的光学收集技术称为拉曼光谱。这为检测眼睛中的生物标志物提供了一种非侵入性、高灵敏度的方法，但它可以被包装成低成本的手持式设备。为了提供如此灵敏和快速的诊断技术，准确识别这些源自眼睛不同部位的专门拉曼信号至关重要。为了应对这些挑战，将开发被称为“机器学习”的先进计算方法，以接受来自数据的“噪音”，并实现智能诊断的通用框架。与传统的软件包不同，这种方法将使用云技术作为开源直接在Web浏览器中执行数据分析。在此基础上，这项创新技术将允许TBI测量在护理点进行，通过对眼睛后部的非电离扫描来检测生化变化，而不需要进行痛苦的腰椎穿刺（提取脑脊液）或昂贵，危险的放射扫描。我们的主要目标是提供一种技术，为患有神经创伤的患者提供更好的健康，更有效的患者护理和更好的生活质量。它将被设计为由医生和护理人员现场使用，以提供及时和具有成本效益的诊断和分流，并将被救护车信托基金，体育组织，全科医生，医院和国防部使用。在早期临床阶段以非侵入性、具有成本效益的方式进行快速诊断，将为个性化医疗和管理的一系列改进奠定基础。我们的设备主要集中在及时的TBI检测上，可以更好地对患者进行分诊，减少医疗系统的压力。除了为全国近100万人提供及时的干预和有组织的创伤护理外，它还将减缓患者的认知能力下降，降低住院死亡率，每年挽救数千人的生命，避免长期住院，并减轻NHS和纳税人的主要负担。

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality worldwide, with a high complication rate requiring long-term care, creates prolonged post-traumatic neurological disorders and is potentially fatal with annual socioeconomic cost in the UK of £7.5 Billion per year. While critical decisions affecting treatment must be made rapidly, TBI is notoriously hard to diagnose pre-hospitalisation, sometimes resulting in incorrect patient management. Timely assessment of injury severity is a priority in the correct treatment of TBI patients. However, this is poorly supported by current technologies, which fall short of the diagnostic needs, exhibiting poor-sensitivity, special-handling requirements and complicated, costly procedures.A non-invasive portable technique to diagnose and monitor TBI and neurodegenerative diseases is proposed, by measuring changes to the optic nerve, visible at the back of the eye. The optic nerve, bathed in cerebrospinal fluid, which is in continuity with the central nervous system, constitutes an optically clear 'window to the brain'. The aim is to develop a portable technology to detect biochemical changes in cerebrospinal fluid in response to brain injury using specialised optical collected using a technique known as Raman spectroscopy. This provides a non-invasive, highly-sensitive method for the detection of biomarkers in the eye, and yet it can be packaged as a low-cost, hand-held device. For delivering such a sensitive and rapid diagnostic technology, it is crucial to accurately identify these specialised Raman signals originating from different parts of the eye. To tackle these challenges, advanced computational methods, known as "machine learning", will be developed to embrace the 'noise' from the data and enable a generic framework for intelligent diagnosis. Unlike traditional packages, this method will perform data analysis directly in the web browser using cloud technologies as an open-source. Building upon these, this innovative technology will allow TBI measurements to take place at the point-of-care via a non-ionizing scan of the back-of-the-eye to detect biochemical changes without the need for a painful lumbar puncture (to extract the cerebrospinal fluid) or expensive, dangerous radiological scans. Our prime objective is to deliver a technology offering improved health, more effective patient-care and a better quality of life for patients suffering from neurotrauma. It will be designed for use on-site by doctors and paramedic crews to provide timely and cost-effective diagnosis and triaging and will be used by ambulance trusts, sports organisations, GPs, hospitals and the Ministry of Defence. Rapid diagnosis in the early-clinical phase in a non-invasive, cost-effective way will lay the platform for a range of improvements in personalised medicine and management. Predominantly focussed on timely TBI-detection, our device would allow for better patient triaging, reducing the strain on the healthcare system. In addition to delivering the timely intervention and organised trauma-care to nearly a million individuals nationally, it will decelerate the patients' cognitive decline, reduce in-hospital mortality, save thousands of lives a year, avoid long-term hospital stays, and reduce a major burden on the NHS and the taxpayer.