Artificial intelligence in the clinical laboratory

Hou, Hanjing; Zhang, Rui; Li, Jinming

doi:10.1016/j.cca.2024.119724

Clinica Chimica Acta

2024

DOI: 10.1016/j.cca.2024.119724

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Artificial intelligence in the clinical laboratory

Hanjing Hou,

Rui Zhang,

Jinming Li

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2024

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Cited by 4 publications

References 110 publications

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Development and optimization of diagnostic assays for infectious diseases

Ciotti,

Nicolai,

Pieri

2024

LabMed Discovery

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Development and optimization of diagnostic assays for infectious diseases

Ciotti,

Nicolai,

Pieri

2024

LabMed Discovery

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Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective Multicenter Study

Seok,

Yu,

Shin

et al. 2024

Clinical Chemistry

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Background In clinical laboratories, the precision and sensitivity of autoverification technologies are crucial for ensuring reliable diagnostics. Conventional methods have limited sensitivity and applicability, making error detection challenging and reducing laboratory efficiency. This study introduces a machine learning (ML)-based autoverification technology to enhance tumor marker test error detection. Methods The effectiveness of various ML models was evaluated by analyzing a large data set of 397 751 for model training and internal validation and 215 339 for external validation. Sample misidentification was simulated by random shuffling error-free test results with a 1% error rate to achieve a real-world approximation. The ML models were developed with Bayesian optimization for tuning. Model validation was performed internally at the primary institution and externally at other institutions, comparing the ML models’ performance with conventional delta check methods. Results Deep neural networks and extreme gradient boosting achieved an area under the receiver operating characteristic curve of 0.834 to 0.903, outperforming that of conventional methods (0.705 to 0.816). External validation by 3 independent laboratories showed that the balanced accuracy of the ML model ranged from 0.760 to 0.836, outperforming the balanced accuracy of 0.670 to 0.773 of the conventional models. Conclusions This study addresses limitations regarding the sensitivity of current delta check methods for detection of sample misidentification errors and provides versatile models that mitigate the operational challenges faced by smaller laboratories. Our findings offer a pathway toward more efficient and reliable clinical laboratory testing.

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Diagnosis Based on Population Data versus Personalized Data: The Evolving Paradigm in Laboratory Medicine

Coskun

2024

Diagnostics

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The diagnosis of diseases is a complex process involving the integration of multiple parameters obtained from various sources, including laboratory findings. The interpretation of laboratory data is inherently comparative, necessitating reliable references for accurate assessment. Different types of references, such as reference intervals, decision limits, action limits, and reference change values, are essential tools in the interpretation of laboratory data. Although these references are used to interpret individual laboratory data, they are typically derived from population data, which raises concerns about their reliability and consequently the accuracy of interpretation of individuals’ laboratory data. The accuracy of diagnosis is critical to all subsequent steps in medical practice, making the estimate of reliable references a priority. For more precise interpretation, references should ideally be derived from an individual’s own data rather than from population averages. This manuscript summarizes the current sources of references used in laboratory data interpretation, examines the references themselves, and discusses the transition from population-based laboratory medicine to personalized laboratory medicine.

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Artificial intelligence in the clinical laboratory

Cited by 4 publications

References 110 publications

Development and optimization of diagnostic assays for infectious diseases

Development and optimization of diagnostic assays for infectious diseases

Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective Multicenter Study

Diagnosis Based on Population Data versus Personalized Data: The Evolving Paradigm in Laboratory Medicine

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