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无创技术，用于评估视觉途径中的病理或功能障碍。 VF测试与其他诊断症结合使用，用于检测游泳瘤并遵循其进展。早期检测至关重要，因为在几乎所有情况下都可以预防青光眼的失明，并且在进展早期接受治疗。需要一种自动决策援助工具，该工具将促进和标准化解释任务。在成功的I期可行性演示之后，第二阶段将采用新颖的机器学习方法来解决青光眼问题 通过对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，该程序将对青光眼和非透明体缺陷进行可靠的详细分类，并主要强调青光眼缺陷和早期检测。目的是实现与高技能人类专家的分类精度。该软件建议的诊断将由从分类算法中提取的一组综合规则支持。可选地，该程序将提供视野和青光眼进展的度量。