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.

总结

项目成果

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.