Statistical Unsupervised Learning VF for IIHTT & ONTT

摘要 目前对视野测试的评估依赖于算法，主要是为了诊断和 监测青光眼的进展，或对缺陷进行专家描述性分类。算法不起作用 对于非青光眼的视神经病变，因为这些疾病既可以改善，也可以恶化。描述性 分类不容易量化来评估随时间的变化。无监督统计学习 原型分析是研究青光眼和潜在的其他视神经疾病的一种新方法。两者都有 特发性颅内高压和视神经炎是通常可以改善和对治疗有反应的疾病。 NEI赞助的两项针对每种疾病的临床试验的视野原型分析，ONTT和 将对IIHTT进行调查，以确定这些发现是否与已报道的治疗结果和效果平行。 我们还将测试机器学习的可量化原型，即与疾病相关的模式 视野缺陷，类似于专家的判断，是否对视神经功能的变化敏感， 如果他们发现，根据先前的研究标准，报告正常的眼睛中存在残余的视神经功能障碍。添加 来自临床的IIH和视神经炎的病例将增强每种疾病的原型以用于临床。 和新的研究。

Summary Current assessments of visual field testing depend on algorithms, principally developed to diagnose and monitor progression in glaucoma, or on expert descriptive categorization of deficits. The algorithms do not work well for non-glaucomatous optic neuropathies as these disorders can both improve and deteriorate. Descriptive categorizations are not readily quantifiable to assess change over time. Unsupervised statistical learning archetypal analysis is a new way to investigate glaucoma and potentially other optic neuropathies. Both idiopathic intracranial hypertension and optic neuritis are disorders that often improve and respond to therapy. Archetypal analysis of the visual fields from two NEI sponsored clinical trials on each disorder, ONTT and IIHTT, will be investigated to determine if the findings parallel the reported outcomes and effects of therapy. We will also test whether machine learning quantifiable archetypes, which are disease-associated patterns of field deficits, are similar to expert determinations, whether they are sensitive to changes in optic nerve function, and if they reveal residual optic nerve dysfunction in eyes reported to be normal by prior study criteria. Adding cases of IIH and optic neuritis from the clinic will enhance the archetypes for each disorder for use in the clinic and new studies.