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Artificial Intelligence Applications in Clinical Chemistry

  • Author Footnotes
    1 Denotes co-first authors with equal contributions.
    Dustin R. Bunch
    Footnotes
    1 Denotes co-first authors with equal contributions.
    Affiliations
    Department of Pathology and Laboratory Medicine, Nationwide Children’s Hospital, 700 Children’s Drive, C1923, Columbus, OH 43205-2644, USA

    Department of Pathology, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
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  • Author Footnotes
    1 Denotes co-first authors with equal contributions.
    Thomas JS. Durant
    Footnotes
    1 Denotes co-first authors with equal contributions.
    Affiliations
    Department of Laboratory Medicine, Yale School of Medicine, 55 Park Street, Room PS 502A, New Haven, CT 06510, USA
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  • Joseph W. Rudolf
    Correspondence
    Corresponding author.
    Affiliations
    Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA

    ARUP Laboratories, 500 Chipeta Way, MC 115, Salt Lake City, UT 84108, USA
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  • Author Footnotes
    1 Denotes co-first authors with equal contributions.
Published:December 16, 2022DOI:https://doi.org/10.1016/j.cll.2022.09.005

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      References

        • Herman D.S.
        • Rhoads D.D.
        • Schulz W.L.
        • et al.
        Artificial intelligence and mapping a new direction in laboratory medicine: a review.
        Clin Chem. 2021; 67: 1466-1482
        • Smith K.P.
        • Wang H.
        • Durant T.J.S.
        • et al.
        Applications of artificial intelligence in clinical microbiology testing.
        Clin Microbiol Newsl. 2020; 42: 61-70
        • Lima-Oliveira G.
        • Volanski W.
        • Lippi G.
        • et al.
        Pre-analytical phase management: a review of the procedures from patient preparation to laboratory analysis.
        Scand J Clin Lab Invest. 2017; 77: 153-163
        • Alavi N.
        • Khan S.H.
        • Saadia A.
        • et al.
        Challenges in preanalytical phase of laboratory medicine: rate of blood sample nonconformity in a tertiary care hospital.
        EJIFCC. 2020; 31: 21-27
        • Carraro P.
        • Plebani M.
        Errors in a stat laboratory: types and frequencies 10 years later.
        Clin Chem. 2007; 53: 1338-1342
        • Dzik W.H.
        • Murphy M.F.
        • Andreu G.
        • et al.
        An international study of the performance of sample collection from patients.
        Vox Sang. 2003; 85: 40-47
        • Rosenbaum M.W.
        • Baron J.M.
        Using machine learning-based multianalyte delta checks to detect wrong blood in tube errors.
        Am J Clin Pathol. 2018; 150: 555-566
        • Mitani T.
        • Doi S.
        • Yokota S.
        • et al.
        Highly accurate and explainable detection of specimen mix-up using a machine learning model.
        Clin Chem Lab Med. 2020; 58: 375-383
        • Zhou R.
        • Liang Y.F.
        • Cheng H.L.
        • et al.
        A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.
        Clin Chem Lab Med. 2021; https://doi.org/10.1515/cclm-2021-1171
        • Farrell C.L.
        Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors.
        Clin Chem Lab Med. 2021; https://doi.org/10.1515/cclm-2021-0873
        • Farrell C.J.
        Identifying mislabelled samples: machine learning models exceed human performance.
        Ann Clin Biochem. 2021; 58: 650-652
        • Baron J.M.
        • Mermel C.H.
        • Lewandrowski K.B.
        • et al.
        Detection of preanalytic laboratory testing errors using a statistically guided protocol.
        Am J Clin Pathol. 2012; 138: 406-413
        • Benirschke R.C.
        • Gniadek T.J.
        Detection of falsely elevated point-of-care potassium results due to hemolysis using predictive analytics.
        Am J Clin Pathol. 2020; 154: 242-247
        • Bigorra L.
        • Larriba I.
        • Gutierrez-Gallego R.
        Machine learning algorithms for the detection of spurious white blood cell differentials due to erythrocyte lysis resistance.
        J Clin Pathol. 2019; 72: 431-437
        • Yang C.
        • Li D.
        • Sun D.
        • et al.
        A deep learning-based system for assessment of serum quality using sample images.
        Clin Chim Acta. 2022; 531: 254-260
        • Shi X.
        • Deng Y.
        • Fang Y.
        • et al.
        A hemolysis image detection method based on GAN-CNN-ELM.
        Comput Math Methods Med. 2022; 2022: 1558607
        • Fang K.
        • Dong Z.
        • Chen X.
        • et al.
        Using machine learning to identify clotted specimens in coagulation testing.
        Clin Chem Lab Med. 2021; 59: 1289-1297
        • Ng D.
        • Polito F.A.
        • Cervinski M.A.
        Optimization of a moving averages program using a simulated annealing algorithm: the goal is to monitor the process not the patients.
        Clin Chem. 2016; 62: 1361-1371
        • van Rossum H.H.
        Moving average quality control: principles, practical application and future perspectives.
        Clin Chem Lab Med. 2019; 57: 773-782
        • van Rossum H.H.
        • van den Broek D.
        Ten-month evaluation of the routine application of patient moving average for real-time quality control in a hospital setting.
        J Appl Lab Med. 2020; 5: 1184-1193
        • Smith J.D.
        • Badrick T.
        • Bowling F.
        A direct comparison of patient-based real-time quality control techniques: the importance of the analyte distribution.
        Ann Clin Biochem. 2020; 57: 206-214
        • Loh T.P.
        • Bietenbeck A.
        • Cervinski M.A.
        • et al.
        Recommendation for performance verification of patient-based real-time quality control.
        Clin Chem Lab Med. 2020; 58: 1205-1213
        • Sampson M.L.
        • Gounden V.
        • van Deventer H.E.
        • et al.
        CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.
        Clin Biochem. 2016; 49: 201-207
        • Toghi Eshghi S.
        • Auger P.
        • Mathews W.R.
        Quality assessment and interference detection in targeted mass spectrometry data using machine learning.
        Clin Proteomics. 2018; 15: 33
        • Kuligowski J.
        • Sanchez-Illana A.
        • Sanjuan-Herraez D.
        • et al.
        Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support vector regression (QC-SVRC).
        Analyst. 2015; 140: 7810-7817
        • Valderrama C.E.
        • Niven D.J.
        • Stelfox H.T.
        • et al.
        Predicting abnormal laboratory blood test results in the intensive care unit using novel features based on information theory and historical conditional probability: observational study.
        JMIR Med Inform. 2022; 10: e35250
        • Lidbury B.A.
        • Richardson A.M.
        • Badrick T.
        Assessment of machine-learning techniques on large pathology data sets to address assay redundancy in routine liver function test profiles.
        Diagnosis (Berl). 2015; 2: 41-51
        • Luo Y.
        • Szolovits P.
        • Dighe A.S.
        • et al.
        Using machine learning to predict laboratory test results.
        Am J Clin Pathol. 2016; 145: 778-788
        • Kurstjens S.
        • de Bel T.
        • van der Horst A.
        • et al.
        Automated prediction of low ferritin concentrations using a machine learning algorithm.
        Clin Chem Lab Med. 2022; https://doi.org/10.1515/cclm-2021-1194
        • Luo Y.
        • Szolovits P.
        • Dighe A.S.
        • et al.
        3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data.
        J Am Med Inform Assoc. 2018; 25: 645-653
        • Duffy M.J.
        Biomarkers for prostate cancer: prostate-specific antigen and beyond.
        Clin Chem Lab Med. 2020; 58: 326-339
        • Catalona W.J.
        • Partin A.W.
        • Sanda M.G.
        • et al.
        A multicenter study of [-2]pro-prostate specific antigen combined with prostate specific antigen and free prostate specific antigen for prostate cancer detection in the 2.0 to 10.0 ng/ml prostate specific antigen range.
        J Urol. 2011; 185: 1650-1655
        • Parekh D.J.
        • Punnen S.
        • Sjoberg D.D.
        • et al.
        A multi-institutional prospective trial in the USA confirms that the 4Kscore accurately identifies men with high-grade prostate cancer.
        Eur Urol. 2015; 68: 464-470
        • Yilmaz Y.
        • Eren F.
        Identification of a support vector machine-based biomarker panel with high sensitivity and specificity for nonalcoholic steatohepatitis.
        Clin Chim Acta. 2012; 414: 154-157
        • Woreta T.A.
        • Van Natta M.L.
        • Lazo M.
        • et al.
        Validation of the accuracy of the FAST score for detecting patients with at-risk nonalcoholic steatohepatitis (NASH) in a North American cohort and comparison to other non-invasive algorithms.
        PLoS One. 2022; 17: e0266859
        • Chan L.
        • Nadkarni G.N.
        • Fleming F.
        • et al.
        Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease.
        Diabetologia. 2021; 64: 1504-1515
        • Connolly P.
        • Stapleton S.
        • Mosoyan G.
        • et al.
        Analytical validation of a multi-biomarker algorithmic test for prediction of progressive kidney function decline in patients with early-stage kidney disease.
        Clin Proteomics. 2021; 18: 26
        • Mathioudakis N.N.
        • Abusamaan M.S.
        • Shakarchi A.F.
        • et al.
        Development and validation of a machine learning model to predict near-term risk of iatrogenic hypoglycemia in hospitalized patients.
        JAMA Netw Open. 2021; 4: e2030913
        • Tomasev N.
        • Glorot X.
        • Rae J.W.
        • et al.
        A clinically applicable approach to continuous prediction of future acute kidney injury.
        Nature. 2019; 572: 116-119
        • Chiofolo C.
        • Chbat N.
        • Ghosh E.
        • et al.
        Automated continuous acute kidney injury prediction and surveillance: a random forest model.
        Mayo Clin Proc. 2019; 94: 783-792
        • Anand R.S.
        • Stey P.
        • Jain S.
        • et al.
        Predicting mortality in diabetic ICU patients using machine learning and severity indices.
        AMIA Jt Summits Transl Sci Proc. 2018; 2017: 310-319
        • Wilkes E.H.
        • Rumsby G.
        • Woodward G.M.
        Using machine learning to aid the interpretation of urine steroid profiles.
        Clin Chem. 2018; 64: 1586-1595
        • Wilkes E.H.
        • Emmett E.
        • Beltran L.
        • et al.
        A machine learning approach for the automated interpretation of plasma amino acid profiles.
        Clin Chem. 2020; 66: 1210-1218
        • Eisenhofer G.
        • Duran C.
        • Cannistraci C.V.
        • et al.
        Use of steroid profiling combined with machine learning for identification and subtype classification in primary aldosteronism.
        JAMA Netw Open. 2020; 3: e2016209
        • Laengsri V.
        • Shoombuatong W.
        • Adirojananon W.
        • et al.
        ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia.
        BMC Med Inform Decis Mak. 2019; 19: 212
        • Weis C.
        • Cuenod A.
        • Rieck B.
        • et al.
        Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning.
        Nat Med. 2022; 28: 164-174
        • Feretzakis G.
        • Sakagianni A.
        • Loupelis E.
        • et al.
        Machine learning for antibiotic resistance prediction: a prototype using off-the-shelf techniques and entry-level data to guide empiric antimicrobial therapy.
        Healthc Inform Res. 2021; 27: 214-221
        • Tzelves L.
        • Lazarou L.
        • Feretzakis G.
        • et al.
        Using machine learning techniques to predict antimicrobial resistance in stone disease patients.
        World J Urol. 2022; https://doi.org/10.1007/s00345-022-04043-x
        • Waljee A.K.
        • Sauder K.
        • Patel A.
        • et al.
        Machine learning algorithms for objective remission and clinical outcomes with thiopurines.
        J Crohns Colitis. 2017; 11: 801-810
        • Hawker C.D.
        • McCarthy W.
        • Cleveland D.
        • et al.
        Invention and validation of an automated camera system that uses optical character recognition to identify patient name mislabeled samples.
        Clin Chem. 2014; 60: 463-470
        • Oyaert M.
        • Delanghe J.
        Progress in automated urinalysis.
        Ann Lab Med. 2019; 39: 15-22
        • U.S. Food and Drug Administration
        DIP/U.S. Urine analysis test system K173327 approval letter.
        (Available at:) (Accessed July 5, 2022)
        • Smith G.T.
        • Dwork N.
        • Khan S.A.
        • et al.
        Robust dipstick urinalysis using a low-cost, micro-volume slipping manifold and mobile phone platform.
        Lab Chip. 2016; 16: 2069-2078
        • Bakan E.
        • Bayraktutan Z.
        • Baygutalp N.K.
        • et al.
        Evaluation of the analytical performances of Cobas 6500 and Sysmex UN series automated urinalysis systems with manual microscopic particle counting.
        Biochem Med (Zagreb). 2018; 28: 020712
        • Liang Y.
        • Kang R.
        • Lian C.
        • et al.
        An end-to-end system for automatic urinary particle recognition with convolutional neural network.
        J Med Syst. 2018; 42: 165
        • Ince F.D.
        • Ellidag H.Y.
        • Koseoglu M.
        • et al.
        The comparison of automated urine analyzers with manual microscopic examination for urinalysis automated urine analyzers and manual urinalysis.
        Pract Lab Med. 2016; 5: 14-20
        • Laiwejpithaya S.
        • Wongkrajang P.
        • Reesukumal K.
        • et al.
        UriSed 3 and UX-2000 automated urine sediment analyzers vs manual microscopic method: a comparative performance analysis.
        J Clin Lab Anal. 2018; 32https://doi.org/10.1002/jcla.22249
        • Linko S.
        • Kouri T.T.
        • Toivonen E.
        • et al.
        Analytical performance of the Iris iQ200 automated urine microscopy analyzer.
        Clin Chim Acta. 2006; 372: 54-64
        • Durant T.J.S.
        • Olson E.M.
        • Schulz W.L.
        • et al.
        Very deep convolutional neural networks for morphologic classification of erythrocytes.
        Clin Chem. 2017; 63: 1847-1855
        • Chandradevan R.
        • Aljudi A.A.
        • Drumheller B.R.
        • et al.
        Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.
        Lab Invest. 2020; 100: 98-109
        • Durant T.J.S.
        • Dudgeon S.N.
        • McPadden J.
        • et al.
        Applications of digital microscopy and densely connected convolutional neural networks for automated quantification of babesia-infected erythrocytes.
        Clin Chem. 2021; 68: 218-229
        • Satoh M.
        • Vazquez-Del Mercado M.
        • Chan E.K.
        Clinical interpretation of antinuclear antibody tests in systemic rheumatic diseases.
        Mod Rheumatol. 2009; 19: 219-228
        • De Bruyne S.
        • Speeckaert M.M.
        • Van Biesen W.
        • et al.
        Recent evolutions of machine learning applications in clinical laboratory medicine.
        Crit Rev Clin Lab Sci. 2021; 58: 131-152
        • Park Y.
        • Kim S.Y.
        • Kwon G.C.
        • et al.
        Automated versus conventional microscopic interpretation of antinuclear antibody indirect immunofluorescence test.
        Ann Clin Lab Sci. 2019; 49: 127-133
        • Nagy G.
        • Csipo I.
        • Tarr T.
        • et al.
        Anti-neutrophil cytoplasmic antibody testing by indirect immunofluorescence: computer-aided versus conventional microscopic evaluation of routine diagnostic samples from patients with vasculitis or other inflammatory diseases.
        Clin Chim Acta. 2020; 511: 117-124
        • Wu Y.D.
        • Sheu R.K.
        • Chung C.W.
        • et al.
        Application of supervised machine learning to recognize competent level and mixed antinuclear antibody patterns based on ICAP international consensus.
        Diagnostics (Basel). 2021; 11https://doi.org/10.3390/diagnostics11040642
        • Punchoo R.
        • Bhoora S.
        • Pillay N.
        Applications of machine learning in the chemical pathology laboratory.
        J Clin Pathol. 2021; 74: 435-442
      1. Li H, Racine-Brzostek S, Xi N, Luo J, Zhao Z, Yuan J. Learning to Detect Monoclonal Protein in Electrophoresis Images. 2021 International Conference on Visual Communications and Image Processing (VCIP). 2021:1-5. doi:10.1109/VCIP53242.2021.9675332

        • Wei X.Y.
        • Yang Z.Q.
        • Zhang X.L.
        • et al.
        Deep collocative learning for immunofixation electrophoresis image analysis.
        IEEE Trans Med Imaging. 2021; 40: 1898-1910
        • Chabrun F.
        • Dieu X.
        • Ferre M.
        • et al.
        Achieving expert-level interpretation of serum protein electrophoresis through deep learning driven by human reasoning.
        Clin Chem. 2021; 67: 1406-1414
        • Santilli A.M.L.
        • Ren K.
        • Oleschuk R.
        • et al.
        Application of intraoperative mass spectrometry and data analytics for oncological margin detection, a review.
        IEEE Trans Biomed Eng. 2022; 69: 2220-2232
        • Kim J.I.
        • Maguire F.
        • Tsang K.K.
        • et al.
        Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective.
        Clin Microbiol Rev. 2022; : e0017921https://doi.org/10.1128/cmr.00179-21
        • Vicente F.B.
        • Lin D.C.
        • Haymond S.
        Automation of chromatographic peak review and order to result data transfer in a clinical mass spectrometry laboratory.
        Clin Chim Acta. 2019; 498: 84-89
        • Eilertz D.
        • Mitterer M.
        • Buescher J.M.
        automRm: an r package for fully automatic LC-QQQ-MS data preprocessing powered by machine learning.
        Anal Chem. 2022; 94: 6163-6171
        • Yu M.
        • Bazydlo L.A.L.
        • Bruns D.E.
        • et al.
        Streamlining quality review of mass spectrometry data in the clinical laboratory by use of machine learning.
        Arch Pathol Lab Med. 2019; 143: 990-998
        • Peng G.
        • Tang Y.
        • Cowan T.M.
        • et al.
        Reducing false-positive results in newborn screening using machine learning.
        Int J Neonatal Screen. 2020; 6https://doi.org/10.3390/ijns6010016
        • Subhashini P.
        • Jaya Krishna S.
        • Usha Rani G.
        • et al.
        Application of machine learning algorithms for the differential diagnosis of peroxisomal disorders.
        J Biochem. 2019; 165: 67-73
        • Labriffe M.
        • Woillard J.B.
        • Debord J.
        • et al.
        Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles.
        CPT Pharmacometrics Syst Pharmacol. 2022; https://doi.org/10.1002/psp4.12810
        • Woillard J.B.
        • Labriffe M.
        • Debord J.
        • et al.
        Mycophenolic acid exposure prediction using machine learning.
        Clin Pharmacol Ther. 2021; 110: 370-379
        • Burghelea D.
        • Moisoiu T.
        • Ivan C.
        • et al.
        The use of machine learning algorithms and the mass spectrometry lipidomic profile of serum for the evaluation of tacrolimus exposure and toxicity in kidney transplant recipients.
        Biomedicines. 2022; 10https://doi.org/10.3390/biomedicines10051157
        • Chary M.
        • Boyer E.W.
        • Burns M.M.
        Diagnosis of Acute Poisoning using explainable artificial intelligence.
        Comput Biol Med. 2021; 134: 104469
        • Randell E.W.
        • Yenice S.
        • Khine Wamono A.A.
        • et al.
        Autoverification of test results in the core clinical laboratory.
        Clin Biochem. 2019; 73: 11-25
        • Demirci F.
        • Akan P.
        • Kume T.
        • et al.
        Artificial neural network approach in laboratory test reporting: learning algorithms.
        Am J Clin Pathol. 2016; 146: 227-237https://doi.org/10.1093/ajcp/aqw104
        • Wang H.
        • Wang H.
        • Zhang J.
        • et al.
        Using machine learning to develop an autoverification system in a clinical biochemistry laboratory.
        Clin Chem Lab Med. 2021; 59: 883-891
        • Hoffmann R.G.
        Statistics in the practice of medicine.
        JAMA. 1963; 185: 864-873
        • Bhattacharya C.G.
        A simple method of resolution of a distribution into Gaussian components.
        Biometrics. 1967; 23: 115-135
        • Jones G.R.D.
        • Haeckel R.
        • Loh T.P.
        • et al.
        Indirect methods for reference interval determination - review and recommendations.
        Clin Chem Lab Med. 2018; 57: 20-29
        • Poole S.
        • Schroeder L.F.
        • Shah N.
        An unsupervised learning method to identify reference intervals from a clinical database.
        J Biomed Inform. 2016; 59: 276-284
        • Yang Q.
        • Mwenda K.M.
        • Ge M.
        Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate.
        Int J Health Geogr. 2013; 12: 11
        • Jackson B.R.
        • Ye Y.
        • Crawford J.M.
        • et al.
        The ethics of artificial intelligence in pathology and laboratory medicine: principles and practice.
        Acad Pathol. 2021; 8 (2374289521990784)https://doi.org/10.1177/2374289521990784
        • Schulz W.L.
        • Durant T.J.S.
        • Krumholz H.M.
        Validation and regulation of clinical artificial intelligence.
        Clin Chem. 2019; 65: 1336-1337
        • Char D.S.
        • Abramoff M.D.
        • Feudtner C.
        Identifying ethical considerations for machine learning healthcare applications.
        Am J Bioeth. 2020; 20: 7-17
        • Vayena E.
        • Blasimme A.
        • Cohen I.G.
        Machine learning in medicine: addressing ethical challenges.
        PLoS Med. 2018; 15: e1002689
        • Mahajan S.M.
        • Heidenreich P.
        • Abbott B.
        • et al.
        Predictive models for identifying risk of readmission after index hospitalization for heart failure: a systematic review.
        Eur J Cardiovasc Nurs. 2018; 17: 675-689
        • Marin M.J.
        • Van Wijk X.M.R.
        • Durant T.J.S.
        Machine learning in healthcare: mapping a path to title 21.
        Clin Chem. 2022; 68: 609-610
        • Wilson F.P.
        • Martin M.
        • Yamamoto Y.
        • et al.
        Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial.
        BMJ. 2021; 372: m4786
        • Lachance P.
        • Villeneuve P.M.
        • Rewa O.G.
        • et al.
        Association between e-alert implementation for detection of acute kidney injury and outcomes: a systematic review.
        Nephrol Dial Transpl. 2017; 32: 265-272
        • Obermeyer Z.
        • Powers B.
        • Vogeli C.
        • et al.
        Dissecting racial bias in an algorithm used to manage the health of populations.
        Science. 2019; 366: 447-453