Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

中文翻译：

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下一代基于患者的实时质量控制模型。


基于患者的实时 QC (PBRTQC) 使用患者来源的数据来评估检测性能。 PBRTQC 算法的发展与计算机科学的发展以及功能更强大的计算机的可用性的提高并行。人工智能在 PBRTQC 中的应用非常迅速，与传统方法相比具有许多明显的优势。然而，直到这次审查之前，还没有对这些进行批判性的比较。描述并对比了基于移动平均、回归调整实时QC、神经网络和异常检测的PBRTQC算法。随着人工智能工具变得更加可供实验室使用、用户友好且计算效率高，其主要缺点（例如复杂性和对高计算资源的需求）已减少，并且在 PBRTQC 应用中实施变得有吸引力。