Next-Generation Patient-Based Real-Time Quality Control Models

Abstract

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.

Keywords: Artificial intelligence; Machine learning; Patient-based real-time QC; QC.

Fig. 1. Summary of three next-generation methods to improve PBRTQC performance.

Abbreviations: PBRTQC, patient-based real-time QC; RARTQC, regression-adjusted real-time QC; NN, neural network; SVM, support vector machine; RF, random forest.