{"title":"基于人工智能监测平台的患者实时质量控制在唐氏综合征血清筛查质量风险持续监测中的应用。","authors":"Xuran Yang, Qianlan Chen, Zhifeng Pan, Jingmao Cheng, Wenting Zheng, Yingliang Liang, Hui Chen, Guanghui Chen, Wandang Wang","doi":"10.1002/jcla.25019","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Patient-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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The 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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Optimal 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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The 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.</p>\n </section>\n </div>","PeriodicalId":15509,"journal":{"name":"Journal of Clinical Laboratory Analysis","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcla.25019","citationCount":"0","resultStr":"{\"title\":\"Application of Patient-Based Real-Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening\",\"authors\":\"Xuran Yang, Qianlan Chen, Zhifeng Pan, Jingmao Cheng, Wenting Zheng, Yingliang Liang, Hui Chen, Guanghui Chen, Wandang Wang\",\"doi\":\"10.1002/jcla.25019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Patient-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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>The 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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Optimal 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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>The 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.</p>\\n </section>\\n </div>\",\"PeriodicalId\":15509,\"journal\":{\"name\":\"Journal of Clinical Laboratory Analysis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcla.25019\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Laboratory Analysis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jcla.25019\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL LABORATORY TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Laboratory Analysis","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jcla.25019","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
Application of Patient-Based Real-Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening
Background
Patient-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.
Methods
The 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.
Results
Optimal 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.
Conclusions
The 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.
期刊介绍：
Journal of Clinical Laboratory Analysis publishes original articles on newly developing modes of technology and laboratory assays, with emphasis on their application in current and future clinical laboratory testing. This includes reports from the following fields: immunochemistry and toxicology, hematology and hematopathology, immunopathology, molecular diagnostics, microbiology, genetic testing, immunohematology, and clinical chemistry.
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