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A multi-model fusion algorithm as a real-time quality control tool for small shift detection.

Rui Zhou1, Yu-Fang Liang2, Hua-Li Cheng2

  • 1Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, PR China; Beijing Center for Clinical Laboratories, Beijing, PR China.

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|July 19, 2022
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Summary
This summary is machine-generated.

Artificial intelligence enhances patient-based real-time quality control (PBRTQC) for total prostate-specific antigen (tPSA) testing. This AI-driven approach significantly improves accuracy and reduces errors in laboratory settings.

Keywords:
Information entropyMachine learningPBRTQCQuality controlSmall shift

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Area of Science:

  • Clinical Chemistry
  • Artificial Intelligence in Medicine
  • Laboratory Quality Control

Background:

  • Patient-based real-time quality control (PBRTQC) offers advantages over traditional methods, including cost reduction and real-time monitoring.
  • Current PBRTQC methods face limitations in accuracy for detecting small errors in laboratory settings.
  • Artificial intelligence (AI) presents a potential solution to enhance PBRTQC accuracy.

Purpose of the Study:

  • To develop and evaluate an AI-based quality control system for detecting small errors in total prostate-specific antigen (tPSA) testing.
  • To improve the accuracy and reliability of PBRTQC in real laboratory environments.
  • To compare the performance of the AI fusion model against existing PBRTQC methods.

Main Methods:

  • Extracted 84,241 deidentified tPSA results from a hospital laboratory information system.
  • Utilized data simulation to augment the dataset, followed by partitioning into independent training and testing sets.
  • Constructed and weighted three machine learning classification models (Random Forest, Support Vector Machine, Deep Neural Network) using information entropy, and developed a multi-model fusion algorithm.

Main Results:

  • The AI fusion model demonstrated superior performance compared to optimal PBRTQC methods, achieving significantly lower MNPeds (less than 12 for all biases).
  • Accuracy (ACC) of the fusion model surpassed existing methods by nearly 100 times, exceeding 0.9 for most biases.
  • The fusion model exhibited substantially lower false positive rates (FPR) than the compared PBRTQC method, recorded at 0.2% and 0.1%.

Conclusions:

  • The developed AI-based fusion model shows outstanding performance in quality control for tPSA testing.
  • The model effectively reduces incorrect and omitting error detections, making it adaptable for real laboratory settings.
  • This AI-driven approach represents a significant advancement in PBRTQC, enhancing diagnostic accuracy and patient safety.