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液沉积物样品中的晶体，铸件及其亚型。该提案旨在通过利用双折射现象来开发一种用于对演员和晶体类型和亚型的自动分类的技术。由此产生的成像和分类技术将允许改善当前可用的自动尿液分析仪器。尿液中的铸造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造和晶体的子类型进行分类。结果，需要对可疑尿液样本进行手动检查，从而浪费时间和资源。解决方案是改善此类仪器使用的分类技术。该项目旨在开发新型的光学和模式识别技术，用于尿液沉积物中的铸件和晶体的微观颗粒分析。由于使用流式细胞仪和图像分析的当前尿液分析仪器的分类能力有限，因此该项目专门打算为尿液中的亚型铸件和晶体添加功能。我们的方法是使用极化显微镜获得新特征，以帮助对晶体，铸件和其他颗粒进行分类。感兴趣的另一个领域是探索这些晶体的磁性特性，并使用新颖的技术来检测和识别磁场对双向反射特性的影响。第一阶段项目的目的是证明双折射的实用性是一种独特的“签名”，可以为分类尿液颗粒提供新功能。如果达到了I期可行性标准，则在第二阶段，我们将构建一个结合了极化光学的自动化尿液分析仪，并结合了电流显微镜和数字图像处理技术。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的工具。当前的自动尿液分析仪器无法识别尿液沉积物样品中的晶体，铸件及其亚型。该提案旨在通过利用双折射现象来开发一种用于对演员和晶体类型和亚型的自动分类的技术。由此产生的成像和分类技术将允许改善当前可用的自动尿液分析仪器。