An enhancement to nGoggle: a VR Goggle for Structural and Functional assessment in Glaucoma

项目总结 对视神经功能丧失和结构损害的评估是每一项典型的评估 在青光眼的诊断和监测中形成。该方案旨在开发硬件系统 这将允许使用便携式脑机接口(BCI)nGogger获取眼底照片 它集成了可穿戴的无线脑电(EEG)系统和头盔显示器，以 监测与视野刺激相关的脑电活动。在上一个项目中，我们开发了 N切换便携式系统以评估视觉功能缺陷，以解决标准的局限性 自动视野检查(SAP)，一种传统但主观的视野测试。我们比较了诊断准确率 NGogger和SAP在区分青光眼患者和健康人中的应用 如果不是比SAP更好的话，至少表现得与SAP一样好，还具有可移植性和客观性的额外优势。 随着智能手机相机的快速发展，现在开发紧凑型自动对焦成为可能 眼底摄像头，并将它们集成到我们的神经监控nGogger中。最近我们开发了一种方法 名为Machine-to-Machine(M2M)，这是一种深度学习算法，经过训练可以分析眼底照片和 预测SPECTRUM提供的视网膜神经纤维层厚度和神经视网膜边缘的定量测量 域光学相干层析成像(SDOCT)，与原始SDOCT具有很高的相关性和一致性 观察。这种方法为使用简单的眼底照片提取定量 关于青光眼神经损伤的信息。通过结合目前由 N与结构评估功能相结合，这一新系统将产生一种非常独特的设备，能够 用于多种眼部疾病的便携式视神经损伤结构和功能评估，不限于 到青光眼，如老年性黄斑变性、视网膜变性和非青光眼视神经- 玫瑰花。 第一阶段SBIR项目的具体目标是：(1)采用高分辨率自动对焦 眼底相机到n切换头戴式显示器，完全符合眼科仪器安全支架- ARDS和眼底相机功能标准，以及(2)演示nGoggle眼底相机的能力 从模型眼捕捉高质量的视神经图像。该系统的成功开发将使 视网膜状况的结构和功能评估都可以使用以下工具轻松、廉价地执行 一种便携式设备。

PROJECT SUMMARY Assessments of functional loss and of structural damage to the optic nerve are typical evaluations per- formed in the diagnosis and monitoring of glaucoma. This proposal aims at developing the hardware system that will allow acquisition of fundus photographs using the nGoggle, a portable Brain-Computer Interface (BCI) which integrates a wearable, wireless, electroencephalogram (EEG) system and a head-mounted display to monitor electrical brain activities associated with visual field stimulation. In a previous project, we developed the nGoggle portable system for the assessment of visual function deficits in order to address limitations of standard automated perimetry (SAP), a traditional but subjective visual field test. We compared the diagnostic accuracies of the nGoggle and SAP in discriminating patients with glaucoma from healthy subjects and found that the BCI performed at least as well, if not better than SAP, with the additional advantage of portability and objectivity. With rapid advancements in smartphone cameras, it is now possible to develop compact auto-focusing fundus cameras and integrate them into our neuro-monitoring nGoggle. Recently we developed an approach named Machine-to-Machine (M2M), a deep learning algorithm that was trained to analyze fundus photos and predict quantitative measurements of retinal nerve fiber layer thickness and neuroretinal rim provided by spectral domain-optical coherence tomography (SDOCT), with high correlation and agreement with the original SDOCT observations. This approach opens up the possibility of using simple fundus photographs to extract quantitative information about neural damage in glaucoma. By combining functional assessment currently provided by the nGoggle with structural assessment capability, this new system would result in a very unique device capable of portable structural and functional assessment of optic nerve damage for a multitude of eye conditions, not limited to glaucoma, such as age-related macular degeneration, retinal degenerations and non-glaucomatous optic neu- ropathies. The specific aims of this Phase I SBIR project are (1) to incorporate a high-resolution auto-focusing fundus camera to the nGoggle head-mounted display in full compliance with ophthalmic instrument safety stand- ards and fundus camera functional standards, and (2) to demonstrate the ability of the nGoggle fundus camera to capture high-quality optical nerve images from a model eye. Successful development of this system will enable both structural and functional assessments of retinal conditions to be performed easily and inexpensively using a portable device.