Improved Automated Urinalysis

改进的自动化尿液分析

• 批准号: 7270783
• 负责人: Fatima Aziz Merchant
• 金额: $ 10.61万
• 依托单位: SOFT IMAGING, LLC
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-24 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7270783
• 关键词: Improved; Automated; Urinalysis

DESCRIPTION (provided by applicant): Current automated urinalysis instruments are unable to recognize crystals, casts, and their subtypes in urine sediment samples. This proposal seeks to develop a technique for automated classification of the types and subtypes of casts and crystals by exploiting birefringence phenomena. The resulting imaging and classifying technique will permit a quantum improvement in currently available automated urinalysis instruments. The casts and crystals in the urine are clinically important indicators of abnormalities. Current automated techniques are unable to classify the sub-types of casts and crystals. As a result, manual examination of suspect urine samples is necessary, thus wasting time and resources. The solution is to improve the classification techniques that such instruments use. This project aims to develop novel optical and pattern recognition techniques for microscopic particle analysis of casts and crystals in urine sediment. Since current urinalysis instruments using flow cytometry and image analysis are limited in their ability to classify the types and subtypes of casts and crystals, this project specifically intends to add functionality for sub-typing casts and crystals in urine. Our approach is to use polarization microscopy to obtain new features to aid classification of crystals, casts and other particles. Another area of interest is to explore the magnetic properties of these crystals and to use novel techniques to detect and identify the crystals by the influence that magnetic fields have on their birefringence properties. The aim of the Phase I project is to demonstrate the utility of birefringence as a unique 'signature' that can give rise to new features for classifying urine particles. If the Phase I feasibility criteria are met, then in Phase II we will build an automated urine particle analyzer incorporating polarization optics combined with current flow microscopy and digital image processing techniques. The resulting automated instrument will provide quicker and more accurate results, and will thus become an even more important tool for urine screening in clinical diagnostics. Current automated urinalysis instruments are unable to recognize crystals, casts, and their subtypes in urine sediment samples. This proposal seeks to develop a technique for automated classification of the types and subtypes of casts and crystals by exploiting birefringence phenomena. The resulting imaging and classifying technique will permit a quantum improvement in currently available automated urinalysis instruments.

描述（由申请人提供）：当前的自动尿液分析仪器无法识别尿沉渣样本中的晶体、管型及其亚型。该提案旨在开发一种利用双折射现象对铸件和晶体的类型和亚型进行自动分类的技术。由此产生的成像和分类技术将使目前可用的自动尿液分析仪器得到巨大的改进。尿液中的管型和晶体是临床上重要的异常指标。当前的自动化技术无法对铸件和晶体的子类型进行分类。因此，需要对可疑尿液样本进行人工检查，从而浪费时间和资源。解决方案是改进此类仪器使用的分类技术。该项目旨在开发新颖的光学和模式识别技术，用于尿沉渣中管型和晶体的微观颗粒分析。由于目前使用流式细胞术和图像分析的尿液分析仪器对管型和晶体的类型和亚型进行分类的能力有限，因此该项目特别打算增加对尿液中管型和晶体进行亚型分类的功能。我们的方法是使用偏光显微镜来获得新特征，以帮助对晶体、铸件和其他颗粒进行分类。另一个感兴趣的领域是探索这些晶体的磁性，并使用新技术通过磁场对其双折射特性的影响来检测和识别晶体。第一阶段项目的目的是展示双折射作为独特“特征”的实用性，可以产生尿液颗粒分类的新功能。如果满足第一阶段的可行性标准，那么在第二阶段，我们将构建一种自动化尿液颗粒分析仪，将偏振光学与当前的流式显微镜和数字图像处理技术相结合。由此产生的自动化仪器将提供更快、更准确的结果，因此将成为临床诊断中尿液筛查的更重要工具。目前的自动尿液分析仪器无法识别尿沉渣样本中的晶体、管型及其亚型。该提案旨在开发一种利用双折射现象对铸件和晶体的类型和亚型进行自动分类的技术。由此产生的成像和分类技术将使目前可用的自动尿液分析仪器得到巨大的改进。