新型视网膜神经节细胞估算模型的建立及对青光眼早期诊断的应用研究

Primary open-angle glaucoma (POAG) is one of the main types of glaucoma. It is usually asymptomatic and difficult to early diagnosis. POAG, which is the first leading cause of irreversible blindness in the world, which is a serious threat to human health. The goal of glaucoma management is to slow down the rate of progressive neural losses and to preserve visual function. Early diagnosis and early intervention of glaucoma play a vital role in the prognosis of the disease and the patient's quality of life. The diagnosis of glaucoma is usually on the basis on the visual field of patients and the characteristic morphology of their optic nerve heads. Mostly the early diagnosis of glaucoma is based on specialist’s skills, as automized optic disc imaging and VF still lack specificity and sensitivity. How to select the appropriate method for early diagnosis of glaucoma is a big challenge in clinical practices. The direction of early clinical diagnosis mainly lies in exploring more effective diagnosis index, rather than blindly adopt new diagnostic technology..Numbers of retinal ganglion cells were estimated using the model described by Medeiros et al, which is based on empirical formulas developed by Harwerth et al for estimating RGC counts from SAP and OCT. Using these formulas, it is possible to use information from both structural and functional tests to derive a final combined estimate of the RGC count in a particular eye. Macula has the highest density of RGCs, macular imaging may be a valuable method of assessing neural damage in glaucoma. Recent studies have shown that the macular ganglion cell-inner plexiform layer(mGCIPL) had similar ability to detect glaucoma compared with circumpapillary RNFL thickness measurements. Therefore we replaced the circumpapillary RNFL thickness and SAP 30-2 by mGCIPL and central 10 degree threshold test in eRGC counts formula. The purpose of this study is to evaluate the new estimated RGC model derived from mGCIPL measurement and SAP of macular in healthy, glaucoma suspect, ocular hypertension and glaucomatous eyes. The new combined structure-functional estimated RGC model is designed to improve early diagnosis of glaucoma by enhanced macular damage measurement. The study investigates to find the best way to establish a clinical practical integration diagnosis model of visual function and optic nerve structure damage. Furthermore, a clinic early diagnosis system and its computer application are developed based on the integrated model. The structure-functional estimated RGC models will be evaluated in clinical practices with prospective three years follow-up and finally provide the evidences to build the new system of primary glaucoma early diagnosis.

原发性开角型青光眼(POAG)是我国主要的青光眼类型之一, 发病隐匿。早期诊断、早期干预对青光眼的诊疗是至关重要的。青光眼的诊断通常是依靠视野为金标准，再加上视神经结构的检测。而早期诊断更多地只能依靠专家的经验来综合判断。如何选用合适的方法早期诊断青光眼是临床的一大难题。临床努力的方向应是探索更加有效的诊断指标,而不是等待新诊断技术的出现。. 本研究利用国外研制的视网膜神经节细胞估算模型的原理，在课题组前期工作的基础上，进行了新的设计，把黄斑区结构和功能测量引入得出黄斑区神经节细胞估算模型，在原有的视神经结构+功能整合基础上，将传统的视盘-视野模型和黄斑结构-功能模型整合在一起，共同来进行青光眼早期诊断和病情进展评估。根据结果进一步设计软件自动进行大数据的分析计算，形成新的青光眼结构与功能损害整合分析方法体系，减少主观偏倚、提高诊断效力，为临床早期诊断青光眼提供一种新的方法。

早期诊断、早期干预对青光眼的诊疗是至关重要的。青光眼的诊断通常是依靠视野为金标准，再加上视神经结构的检测。而早期诊断更多地只能依靠专家的经验来综合判断。如何选用合适的方法早期诊断青光眼是临床的一大难题。本研究利用国外研制的视网膜神经节细胞（Retinal Ganglion cell，RGC）估算模型的原理，在课题组前期工作的基础上，把黄斑区结构和功能测量引入得出黄斑区神经节细胞估算模型。在原有的视神经结构+功能整合基础上，将传统的视盘-视野模型和黄斑结构-功能模型整合在一起，共同来进行青光眼早期诊断和病情进展评估。经过严格的青光眼诊断、随访，采集数据建立了青光眼患者和对照队列，在原有黄斑RGC估算模型基础上进行改良，把结构的检测指标由视盘颞侧周围RNFL厚度测量改为采用黄斑结构参数神经节细胞复合体厚度，SAP的检测程序和部位由较大范围的24-2（间隔6°）改为采用Humphrey视野中央10-2检测程序中的16个位点（间隔2°），聚焦于黄斑固视点10°以内的视网膜敏感度带入公式，建立一个新的黄斑区结构与功能检测的RGC估算模型。通过RGC估算模型三种算法的比较表明黄斑RGC估算值与总eRGC均能够鉴别青光眼和正常眼，通过评估青光眼不同阶段的神经节细胞损失情况，均能够对青光眼患者病情的严重程度进行较好的区分。黄斑RGC估算值改变与青光眼黄斑结构与功能的改变密切相关，且随着病情程度加重，视网膜黄斑区和黄斑以外区域具有类似的神经节细胞损伤程度。黄斑RGC估算值与总eRGC均有较高的诊断效能，通过整合黄斑结构损失和功能损失信息的黄斑RGC估算值有可能成为青光眼黄斑损伤的一项敏感指标。黄斑RGC估算模型的建立为青光眼的诊断和进展分析提供可量化、易重复、简便而高效的对比参数,对青光眼的诊断和青光眼黄斑损伤进展监测有着重要的意义。