Diagnostic aid software for the visual field test

用于视野测试的诊断辅助软件

• 批准号: 7120029
• 负责人: Dariusz Wroblewski
• 金额: $ 32.54万
• 依托单位: BIOFORMATIX, INC.
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-01-02 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7120029
• 关键词: computer human interaction; computer program /software; computer system design /evaluation; diagnosis design /evaluation; disease /disorder classification; early diagnosis; eye disorder diagnosis; glaucoma; human data; mathematics; model design /development; pathologic process; visual fields

Visual field (VF) test is a widely used, noninvasive technique for evaluating pathology or dysfunction in the visual pathways. The VF test, in conjunction with other diagnostics, is used for detection of laucoma and for following its progression. Early detection is critical as blindness from glaucoma is preventable in nearly all cases, provided treatment is administered early in the progression. There is a need for an automated decision aid tool that will facilitate and standardize the interpretation task. Following a successful Phase I feasibility demonstration, Phase II will apply novel machine learning approaches to the problem of glaucoma diagnosis via an automated analysis of visual field and ancillary data. IAC will develop an integrated, user friendly software program that will provide a reliable detailed classification of glaucomatous and non-glaucomatous defects with the main emphasis on glaucomatous defects and early detection. The aim is to achieve classification accuracy close to that of a highly skilled human expert. The diagnosis suggested by the software will be supported by a set of comprehensive rules extracted from the classification algorithm. Optionally, the program will provide measures of visual field and glaucoma progression.

视野(VF)测试是一种广泛使用的非侵入性技术，用于评估视觉通路的病理或功能障碍。VF试验与其他诊断相结合，用于检测月经并跟踪其进展。早期发现是至关重要的，因为青光眼致盲在几乎所有病例中都是可以预防的，只要在青光眼进展的早期给予治疗。需要一种自动化的决策辅助工具，以促进口译工作并使之标准化。继成功的第一阶段可行性论证之后，第二阶段将应用新的机器学习方法来解决青光眼问题 通过视野和辅助数据的自动分析进行诊断。IAC将开发一个集成的、用户友好的软件程序，该程序将提供青光眼和非青光眼缺陷的可靠详细分类，主要侧重于青光眼缺陷和早期检测。其目的是实现接近高技能人类专家的分类精度。软件建议的诊断将得到从分类算法中提取的一组综合规则的支持。可选的是，该计划将提供视野和青光眼进展的测量。