Clinical Cytometry Analysis Software with Automated Gating

描述（由申请人提供）：流式细胞术用于快速收集细胞类型和功能的大量数据。对数十万个细胞进行人工分类的过程成为诊断、高通量筛选、临床试验和大规模研究实验的瓶颈。目前，这一过程需要一名训练有素的技术人员通过手动绘制区域，在数据的数字图形上识别人口。随着数据复杂性的增加，此门控任务变得更加冗长和费力，并且越来越清楚的是，最小化人工处理对于提高吞吐量和一致性至关重要。在临床测试和诊断环境中，自动门控将在标准操作程序（SOP）中消除一组复杂的人工指令和决策，从而减少错误并将结果快速传递给医生。在许多情况下，该软件将能够在医生有机会看到第一份报告之前识别出是否需要进行额外的测试。目前还没有软件可以执行复杂的多参数分析，以自动化和严格验证的方式。FlowDx将填补技术发展的重要空白，为更大规模的表型研究铺平道路，并将这一研究过程转化为临床环境。1)充分定义实验方案，研究人员可以通过比较相同数据集的两个或多个分类来研究人类和算法分类器的差异、偏差和有效性。2)描述和评估比较分类算法性能的指标。3)对我们确定的用例进行分析实验，说明该技术影响临床分析的潜力。4)迭代地实现这些工具来自动化这些实验，提高实验能力，并在新的用例中进行协作。这些目标将在保持软件质量的定量标准、建立系统正常运行时间、吞吐量和健壮性的度量以为后续迭代设置基线的同时得到满足。

DESCRIPTION (provided by applicant): Flow cytometry is used to rapidly gather large quantities of data on cell type and function. The manual process of classifying hundreds of thousands of cells forms a bottleneck in diagnostics, high-throughput screening, clinical trials, and large-scale research experiments. The process currently requires a trained technician to identify populations on a digital graph of the data by manually drawing regions. As the complexity of the data increases, this gating task becomes more lengthy and laborious, and it is increasingly clear that minimizing human processing is essential to increasing both throughput and consistency. In clinical tests and diagnostic environments, automated gating would eliminate a complex set of human instructions and decisions in the Standard Operating Procedure (SOP), thereby reducing error and speeding results to the doctor. In many cases, the software will be able to recognize the need for additional tests before the doctor has an opportunity to look at the first report. Currently no software is available to perform complex multi-parameter analyses in an automated and rigorously validated manner. FlowDx will fill an important gap in the evolution of the technology and pave the way for ever larger phenotypic studies and for the translation of this research process to a clinical environment. Specific Aims 1) Fully define the experimental protocol, whereby a researcher can compare two or more classifications of identical data sets to study the differences, biases and effectiveness of human and algorithmic classifiers. 2) Describe and evaluate metrics that compare the performance of classification algorithms. 3) Conduct analytical experiments on our identified use cases, illustrating the potential of this technique to affect clinical analysis. 4) Iteratively implement the tools to automate these experiments, improve the experimental capabilities, and collaborate in new use cases. These aims will be satisfied while maintaining quantitative standards of software quality, establishing measurements in system uptime, throughput and robustness to set the baseline for subsequent iterations. PUBLIC HEALTH RELEVANCE: FlowDx, a Clinical Cytometry Analysis Software Project is designed to create a new, more efficient, and more effective way of analyzing cells for the presence of cancer, HIV/ AIDS, and other diseases, using a fully automated software system. Using Magnetic Gating, Probability Clustering, Subtractive Cluster Analysis, Artificial Neural Networks, and Support Vector Machines (SVM), Tree Star software will analyze the cell samples from patients at a much faster rate and with fewer false positives and negatives than the manual method now in use. The FlowDx Project 1) Fits the "translational medicine" model of the NIH Roadmap 2) Reduces error in the diagnosis of cancer and other diseases 3) Speeds results to physicians. Patients learn the outcome more quickly. Therapeutic intervention is faster. 4) Accommodates large-scale research by allowing greater volumes of complex data to be much more quickly examined, compared, and quantified 5) Reduces the expense of cell analysis by as much as 50% 6) Conforms to 21CFR Part 11 guidance