Next-Generation Patient-Based Real-Time Quality Control Models
- Author:
Xincen DUAN
1
;
Minglong ZHANG
;
Yan LIU
;
Wenbo ZHENG
;
Chun Yee LIM
;
Sollip KIM
;
Tze Ping LOH
;
Wei GUO
;
Rui ZHOU
;
Tony BADRICK
;
Author Information
- Publication Type:Review Article
- From:Annals of Laboratory Medicine 2024;44(5):385-391
- CountryRepublic of Korea
- Language:English
- 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.
