Applications of machine learning in routine laboratory medicine: Current state and future directions

Rabbani, Naveed; Kim, Grace Y E; Suarez, Carlos J.; Chen, Jonathan

doi:10.1016/j.clinbiochem.2022.02.011

Cited by 50 publications

(20 citation statements)

References 51 publications

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“…However, the results of laboratory tests should be considered comprehensively, including the type of sample, sample quality, and disease of the patient. Moreover, standardization and guidelines for DM analysis have not been established [ 35 ]. Taken together, further research and improvements are anticipated for the complete implementation of DM analyzers in clinical laboratories.…”

Section: Discussionmentioning

confidence: 99%

Performance Assessment of Sysmex DI-60: Is Digital Morphology Analyzer Reliable for White Blood Cell Differentials in Body Fluids?

Shin,

Hur,

Kim

et al. 2024

Diagnostics

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Background: Few studies have evaluated digital morphology (DM) analyzers on body fluids (BF). We evaluated the performance of a DM analyzer, Sysmex DI-60 (Sysmex, Kobe, Japan) for white blood cell (WBC) differentials in BF samples. Methods: In five BF samples (two pleural fluids and three ascites) containing a single, dominant cell type (>80%, neutrophils, lymphocytes, macrophages, abnormal lymphocytes, and malignant cells in each sample), we evaluated the precision of the DI-60 and compared the WBC differentials and turnaround times (TAT) between DI-60 and manual counting. Results: The precision of the DI-60 pre-classification and verification was excellent (%CV, 0.01–3.16%). After verification, the DI-60 showed high sensitivity, specificity, and efficiency (ranges: 90.8–98.1%, 96.8–97.9%, and 92.5–98.0%, respectively) for the dominant cell types in neutrophil- and lymphocyte-dominant samples. For all samples, the DI-60 and manual counting showed high correlations for major cell types (neutrophils, lymphocytes, macrophages, and others, r = 0.72 to 0.94) after verification. The agreement between the pre-classification and verification of the DI-60 was strong in the neutrophil-dominant sample (κ = 0.81). The DI-60 showed a significantly longer TAT (min: s) than manual counting for all samples (median TAT/slide: 6:28 vs. 1:53, p < 0.0001), with remarkable differences in abnormal lymphocyte- and malignant cell-dominant samples (21:05 vs. 2:06; 12:34 vs. 2:25). Conclusions: The DI-60 may provide reliable data in neutrophil- and lymphocyte-dominant BF samples. However, it may require longer times and higher workloads for WBC differentials especially in BF samples containing atypical cells. Further improvement would be needed before applying DM analyzers for routine clinical practice in BF analysis.

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Section: Discussionmentioning

confidence: 99%

Performance Assessment of Sysmex DI-60: Is Digital Morphology Analyzer Reliable for White Blood Cell Differentials in Body Fluids?

Shin,

Hur,

Kim

et al. 2024

Diagnostics

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“…There is some expectation about the use of ML in laboratory medicine; however, there are some concerns that should be addressed, such as data quality, mainly missing data and label error; the cost of the computational infrastructure and the individuals with expertise to develop machine-learning algorithms, as well as ML standardization and regulation required for quality guarantee. At this time, best practices for the clinical validation of ML algorithms should be widely discussed [ 58 , 59 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Routine Laboratory Tests Predict One-Year Cognitive and Functional Decline in a Population Aged 75+ Years

et al. 2023

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Background: Cognitive and functional decline are common problems in older adults, especially in those 75+ years old. Currently, there is no specific plasma biomarker able to predict this decline in healthy old-age people. Machine learning (ML) is a subarea of artificial intelligence (AI), which can be used to predict outcomes Aim: This study aimed to evaluate routine laboratory variables able to predict cognitive and functional impairment, using ML algorithms, in a cohort aged 75+ years, in a one-year follow-up study. Method: One hundred and thirty-two older adults aged 75+ years were selected through a community-health public program or from long-term-care institutions. Their functional and cognitive performances were evaluated at baseline and one year later using a functional activities questionnaire, Mini-Mental State Examination, and the Brief Cognitive Screening Battery. Routine laboratory tests were performed at baseline. ML algorithms—random forest, support vector machine (SVM), and XGBoost—were applied in order to describe the best model able to predict cognitive and functional decline using routine tests as features. Results: The random forest model showed better accuracy than other algorithms and included triglycerides, glucose, hematocrit, red cell distribution width (RDW), albumin, hemoglobin, globulin, high-density lipoprotein cholesterol (HDL-c), thyroid-stimulating hormone (TSH), creatinine, lymphocyte, erythrocyte, platelet/leucocyte (PLR), and neutrophil/leucocyte (NLR) ratios, and alanine transaminase (ALT), leukocyte, low-density lipoprotein cholesterol (LDL-c), cortisol, gamma-glutamyl transferase (GGT), and eosinophil as features to predict cognitive decline (accuracy = 0.79). For functional decline, the most important features were platelet, PLR and NLR, hemoglobin, globulin, cortisol, RDW, glucose, basophil, B12 vitamin, creatinine, GGT, ALT, aspartate transferase (AST), eosinophil, hematocrit, erythrocyte, triglycerides, HDL-c, and monocyte (accuracy = 0.92). Conclusions: Routine laboratory variables could be applied to predict cognitive and functional decline in oldest-old populations using ML algorithms.

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“…Methodological advances, such as instrument digitization, have revolutionized LM over the decades [7]. With vigorous development of computerized technology, a number of computer‐based emerging technologies, such as artificial intelligence (AI) [8], big data [9], machine learning [10], and bioinformatics, such as multi‐omics [11] and genome sequencing analysis [12], are applied to LM. The role of LM is thus expanding.…”

Section: Figurementioning

confidence: 99%

iLABMED, why now and how in the future?

2023

iLABMED

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Nowadays, public health is facing many challenges, mainly including non‐communicable diseases and communicable diseases. Communicable diseases, particularly emerging infectious diseases, have also attracted attention due to their enormous impact on public health and the global economy. The most prominent example is the current global Coronavirus Disease 2019 (COVID‐19) pandemic. The unprecedented and ongoing COVID‐19 pandemic has highlighted the necessity for readily available, accurate, and rapid laboratory medicine (LM) practices. Nevertheless, current LMs and journals in particular have a window for improvement. First, there are limited numbers of professionals available in this field compared to the other disciplines. The current status quo is that most LM manuscripts must be submitted to comprehensive journals or other journals related to the research disease. Second, most LM journals are run by laboratory personnel who are often more concerned with technical advances than with clinical needs. Lastly, several young LM scientists expressed their desire to have a dedicated platform to discuss, communicate, and publish their works on LM. We were therefore motivated to launch iLABMED, an international public forum dedicated to LMs. The establishment of iLABMED adopts the “four I” strategy, namely “Innovation,” “Intelligence,” “Integration,” and “International.” We are attempting to establish a top‐tier journal in the field of LM.

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Applications of machine learning in routine laboratory medicine: Current state and future directions

Cited by 50 publications

References 51 publications

Performance Assessment of Sysmex DI-60: Is Digital Morphology Analyzer Reliable for White Blood Cell Differentials in Body Fluids?

Performance Assessment of Sysmex DI-60: Is Digital Morphology Analyzer Reliable for White Blood Cell Differentials in Body Fluids?

Machine Learning-Based Routine Laboratory Tests Predict One-Year Cognitive and Functional Decline in a Population Aged 75+ Years

iLABMED, why now and how in the future?

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