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

创伤性脑损伤是世界范围内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，会造成长期的创伤后神经疾病，并具有潜在的致命性，每年在英国造成75亿GB的社会经济损失。虽然影响治疗的关键决定必须迅速做出，但众所周知，TBI很难在入院前诊断，有时会导致不正确的患者管理。及时评估损伤严重程度是正确治疗颅脑损伤患者的首要任务。然而，目前的技术不能很好地支持这一点，这些技术不能满足诊断需求，表现出敏感度低、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，通过测量眼睛后面可见的视神经变化来诊断和监测脑外伤和神经退行性疾病。视神经沐浴在脑脊液中，与中枢神经系统保持连续性，构成了一扇通向大脑的光学透明窗口。其目的是开发一种便携式技术，利用一种名为拉曼光谱的技术收集的特殊光学信息来检测脑脊液中的生化变化，以应对脑损伤。这为检测眼睛中的生物标志物提供了一种非侵入性的、高度敏感的方法，但它可以包装成低成本的手持设备。为了提供如此敏感和快速的诊断技术，准确识别这些来自眼睛不同部位的特殊拉曼信号至关重要。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，以容纳数据中的“噪音”，并实现智能诊断的通用框架。与传统的包不同，这种方法将使用云技术作为开源，在Web浏览器中直接执行数据分析。在此基础上，这项创新技术将允许在护理点通过眼后部的非电离扫描进行TBI测量，以检测生化变化，而不需要痛苦的腰椎穿刺术(提取脑脊液)或昂贵而危险的放射扫描。我们的主要目标是提供一种技术，为神经创伤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。它将为医生和护理人员现场使用而设计，以提供及时和经济高效的诊断和分类，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方面的一系列改进奠定平台。我们的设备主要专注于及时的脑损伤检测，可以更好地对患者进行分类，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衰退，降低住院死亡率，每年拯救数千人的生命，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.