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.

概括 目前对视野测试的评估取决于算法，主要用于诊断和 监测青光眼的进展，或对缺陷的专家描述性分类。算法不起作用 对于非亮糖果神经病而言，这些疾病既可以改善和恶化。描述性 分类不容易量化以评估随着时间的流逝。无监督的统计学习 原型分析是研究青光眼和可能其他视力神经病的新方法。两个都 特发性颅内高血压和视神经炎是经常改善和对治疗反应的疾病。 对每个疾病，ONTT和 IIHTT将进行研究，以确定发现是否平行于报告的治疗结果和影响。 我们还将测试机器学习是否可量化原型，这些原型是与疾病相关的模式 现场缺陷，类似于专家确定，它们是否对视神经功能的变化敏感， 如果它们揭示了通过先前的研究标准据报道的眼睛中残留的视神经功能障碍。添加 诊所的IIH和视神经炎病例将增强每种疾病的原型用于临床 和新研究。