NEURAL NETWORKS TO DETECT AND CLASSIFY BLADDER CANCER

用于检测和分类膀胱癌的神经网络

• 批准号: 3493303
• 负责人: ROBERT W VELTRI
• 金额: $ 5万
• 依托单位: UROCOR, INC.
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-03-01 至 1993-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3493303
• 关键词: artificial intelligence; biomarker; biomedical automation; biomedical equipment development; bladder neoplasm; human subject; image enhancement; intracellular transport; neoplasm /cancer diagnosis; neoplastic cell; tumor antigens

CytoDiagnostics, Inc., (CDI) and the University of Oklahoma developed partially automated, user-interactive systems for quantitation of biomarkers used in the diagnosis of bladder cancer. The goal of this Phase I project is to demonstrate the feasibility of further automation with an online neural network system integrated with the quantitative fluorescence image analysis system to classify bladder cancer cells by the pattern of distribution of specific molecules in bladder cancer cells. Researchers at the University of Oklahoma and Tulsa University have collaborated to show the feasibility of first-generation neural network technology to successfully recognize bladder cancer cells from the distribution of DNA within cells. The Phase I project will be a collaboration among CDI and these researchers to initiate development of a commercially feasible system and will: (1) develop a representative grey-level image library of cell types of cells labeled for DNA distributions (Hoechst 33258 dye) and a low grade tumor antigen (M344 antibody), (2) characterize advanced neural networks for discriminating cells off-line, and (3) determine feasibility of developing on-line systems. A phase II SBIR project would develop on-line neural networks integrated with fast image-analysis systems for diagnosis and prognosis of bladder cancer in a commercial diagnostic laboratory.

CytoDiagnostics，Inc.，(CDI)和俄克拉荷马州的大学 部分自动化，用户交互式系统，用于定量 膀胱癌的早期诊断 这个目标 第一阶段的项目是证明进一步自动化的可行性 与在线神经网络系统集成的定量 荧光图像分析系统对膀胱癌细胞进行分类， 膀胱癌中特异性分子的分布模式 细胞 俄克拉荷马州大学和塔尔萨大学的研究人员 合作展示了第一代神经网络的可行性， 网络技术成功识别膀胱癌细胞 DNA在细胞内的分布。 第一阶段项目将是一个 土发委会和这些研究人员之间的合作， 一个商业上可行的系统，并将：（1）开发一个代表性的 标记DNA的细胞类型的灰度图像库 分布（Hoechst 33258染料）和低级肿瘤抗原（M344 抗体），（2）表征用于区分 细胞离线，和（3）确定开发在线的可行性 系统. SBIR项目的第二阶段将开发在线神经网络 与快速图像分析系统集成，用于诊断和预后 在商业诊断实验室里进行膀胱癌的诊断。