Statistical Unsupervised Learning VF for IIHTT & ONTT

IIHTT 的统计无监督学习 VF

• 批准号: 10436319
• 负责人: Mark J Kupersmith
• 金额: $ 16.42万
• 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 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10436319
• 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 目前视野测试的评估取决于算法，主要是为了诊断和 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非常有效，因为这些疾病既可以改善也可以恶化。描述性的 分类不易量化以评估随时间的变化。无监督统计学习 原型分析是研究青光眼和其他潜在视神经病变的新方法。两个都 特发性颅内高压和视神经炎是通常会改善且对治疗有反应的疾病。 对 NEI 赞助的两项针对每种疾病（ONTT 和 ONTT）的临床试验进行的视野原型分析 IIHTT 将进行调查，以确定研究结果是否与报告的治疗结果和效果相符。 我们还将测试机器学习是否可以量化原型，即与疾病相关的模式 视野缺陷，与专家测定类似，是否对视神经功能的变化敏感， 如果它们揭示了眼睛中残留的视神经功能障碍，而根据先前的研究标准，这些视神经功能障碍是正常的。添加 来自诊所的 IIH 和视神经炎病例将增强临床中使用的每种疾病的原型 和新的研究。

项目成果

Specific recommendations to improve the design and conduct of clinical trials.

• DOI: 10.1186/s13063-023-07276-2
• 发表时间: 2023-04-10
• 期刊: Trials
• 影响因子: 2.5

Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders.

• DOI: 10.1167/tvst.13.1.13
• 发表时间: 2024-01-02
• 期刊: TRANSLATIONAL VISION SCIENCE & TECHNOLOGY
• 影响因子: 3
• 作者: Wang, Jui-Kai (Ray);Linton, Edward F.;Johnson, Brett A.;Kupersmith, Mark J.;Garvin, Mona K.;Kardon, Randy H.
• 通讯作者: Kardon, Randy H.