Application of Patient-Based Real-Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening.

Yang, Xuran; Chen, Qianlan; Pan, Zhifeng; Cheng, Jingmao; Zheng, Wenting; Liang, Yingliang; Chen, Hui; Chen, Guanghui; Wang, Wandang

Yang, Xuran; Chen, Qianlan; Pan, Zhifeng; Cheng, Jingmao; Zheng, Wenting; Liang, Yingliang; Chen, Hui; Chen, Guanghui; Wang, Wandang.

Afiliación

Yang X; Department of Clinical Medicine Laboratory, Xiaolan People's Hospital of Zhongshan, Zhongshan, Guangdong, China.
Chen Q; Department of Clinical Medicine Laboratory, Xiaolan People's Hospital of Zhongshan, Zhongshan, Guangdong, China.
Pan Z; Department of Clinical Medicine Laboratory, Xiaolan People's Hospital of Zhongshan, Zhongshan, Guangdong, China.
Cheng J; Department of Clinical Medicine Laboratory, Xiaolan People's Hospital of Zhongshan, Zhongshan, Guangdong, China.
Zheng W; Department of Clinical Medicine Laboratory, Xiaolan People's Hospital of Zhongshan, Zhongshan, Guangdong, China.
Liang Y; Department of Clinical Medicine Laboratory, Xiaolan People's Hospital of Zhongshan, Zhongshan, Guangdong, China.
Chen H; Department of Clinical Medicine Laboratory, Xiaolan People's Hospital of Zhongshan, Zhongshan, Guangdong, China.
Chen G; Department of Clinical Medicine Laboratory, Xiaolan People's Hospital of Zhongshan, Zhongshan, Guangdong, China.
Wang W; Department of Clinical Medicine Laboratory, Xiaolan People's Hospital of Zhongshan, Zhongshan, Guangdong, China.

J Clin Lab Anal ; 38(5): e25019, 2024 Mar.

Article en En | MEDLINE | ID: mdl-38468408

ABSTRACT

ABSTRACT

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.

Asunto(s)

Inteligencia Artificial; Síndrome de Down; Humanos; Síndrome de Down/diagnóstico; Control de Calidad

Palabras clave

AIMA platform; DS serum screening; PBRTQC; quality risk monitoring

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Síndrome de Down Límite: Humans Idioma: En Revista: J Clin Lab Anal Asunto de la revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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