ArticleJournal of clinical laboratory analysis2024
Application of Patient-Based Real-Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening.
Article in Journal of clinical laboratory analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- A comparative study of the quality control efficacy of multiple error-introduction methods for patient-based real-time quality control.Practical laboratory medicine · 2026Article
- A Comparative Study of the Clinical Laboratory Quality Control Performance of AI-PBRTQC and Traditional PBRTQC Model in Tumor Marker Testing.Diagnostics (Basel, Switzerland) · 2026Article
- Patient-based pre-classified real-time quality control with neural network (PCRTQC-NN).Practical laboratory medicine · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundPatient-based real-time quality control (PBRTQC) has gained attention because of its potential to continuously monitor the analytical quality in situations wherein internal quality control (IQC) is less effective. Therefore, we tried to investigate the application of PBRTQC method based on an artificial intelligence monitoring (AI-MA) platform in quality risk monitoring of Down syndrome (DS) serum screening.
methodsThe DS serum screening item determination data and relative IQC data from January 4 to September 7 in 2021 were collected. Then, PBRTQC exponentially weighted moving average (EWMA) and moving average (MA) procedures were built and optimized in the AI-MA platform. The efficiency of the EWMA and MA procedures with intelligent and traditional control rules were compared. Next, the optimal EWMA procedures that contributed to the quality assurance of serum screening were run and generated early warning cases were investigated.
resultsOptimal EWMA and MA procedures on the AI-MA platform were built. Comparison results showed the EWMA procedure with intelligent QC rules but not traditional quality rules contained the best efficiency. Based on the AI-MA platform, two early warning cases were generated by using the optimal EWMA procedure, which finally found were caused by instrument failure. Moreover, the EWMA procedure could truly reflect the detection accuracy and quality in situations wherein traditional IQC products were unstable or concentrations were inappropriate.
conclusionsThe EWMA procedure built by the AI-MA platform could be a good complementary control tool for the DS serum screening by truly and timely reflecting the detection quality risks.
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Registered trials
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