Statistical Unsupervised Learning VF for IIHTT & ONTT

IIHTT 的统计无监督学习 VF

• 批准号: 10192112
• 负责人: Mark J Kupersmith
• 金额: $ 33.12万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10192112
• 关键词: Acute; Affect; Algorithms; Clinic; Clinical Trials; Cost Savings; Detection; Deterioration; Diagnosis; Disease; Evaluation; Event; Eye; Face; Frequencies; Functional disorder; Future; Glaucoma; Head; Injury; Intervention; Intervention Studies; Lead; Machine Learning; Manuals; Masks; Measurement; Measures; Methods; Military Personnel; Monitor; Optic Nerve; Optic Neuritis; Outcome; Outcome Study; Papilledema; Patients; Pattern; Perimetry; Physiologic Intraocular Pressure; Pseudotumor Cerebri; Reader; Reporting; Residual state; Safety; Shapes; Supervision; Testing; Time; Vision; Visit; Visual Fields; Weight; archetypal analysis; base; central visual field; clinical practice; eligible participant; field study; improved; longitudinal analysis; optic nerve disorder; prospective; response; statistical learning; successful intervention; treatment effect; treatment trial; trend; unsupervised learning

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.

总结 目前对视野测试的评估依赖于主要开发用于诊断和 监测青光眼的进展，或专家对缺陷的描述性分类。算法不起作用 对于非青光眼性视神经病变也是如此，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，无法评估一段时间内的变化。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病变的一种新方法。两 特发性颅内高压症和视神经炎是通常对治疗有改善和响应的病症。 对来自NEI赞助的两项关于每种疾病的临床试验（ONT和 将对IIHTT进行研究，以确定结果是否与报告的结局和治疗效果平行。 我们还将测试机器学习可量化的原型，这是疾病相关的模式， 场缺陷，类似于专家的判断，它们是否对视神经功能的变化敏感， 以及是否在先前研究标准报告为正常的眼睛中显示残余视神经功能障碍。添加 来自临床的IIH和视神经炎病例将增强用于临床的每种疾病的原型 和新的研究。