Thomas Durant, MD
Associate Professor of Laboratory MedicineCards
About
Research
Publications
Featured Publications
A primer for quantum computing and its applications to healthcare and biomedical research
Durant T, Knight E, Nelson B, Dudgeon S, Lee S, Walliman D, Young H, Ohno-Machado L, Schulz W. A primer for quantum computing and its applications to healthcare and biomedical research. Journal Of The American Medical Informatics Association 2024, 31: 1774-1784. PMID: 38934288, PMCID: PMC11258415, DOI: 10.1093/jamia/ocae149.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsApplications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes
Durant TJS, Dudgeon SN, McPadden J, Simpson A, Price N, Schulz WL, Torres R, Olson EM. Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes. Clinical Chemistry 2021, 68: 218-229. PMID: 34969114, DOI: 10.1093/clinchem/hvab237.Peer-Reviewed Original ResearchVery Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes
Durant T, Olson EM, Schulz WL, Torres R. Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes. Clinical Chemistry 2017, 63: 1847-1855. PMID: 28877918, DOI: 10.1373/clinchem.2017.276345.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkDeep convolutional neural networkDense shortcut connectionsNeural network designSlow manual processSignificant labor costsClassification of erythrocytesWeb applicationCapable machinesUnseen dataShortcut connectionsManual processEnsemble model predictionsDigital imagesPrecision metricsArchitectural considerationsNetwork designAutomated profilingClassification frequencyMisclassification errorPractical performanceFinal databaseHarmonic meanNetwork
2024
Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels
Spies N, Militello L, Farnsworth C, El-Khoury J, Durant T, Zaydman M. Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels. Clinical Chemistry 2024, hvae168. PMID: 39545815, DOI: 10.1093/clinchem/hvae168.Peer-Reviewed Original ResearchSHapley Additive exPlanationsLearning approachDetection of contamination eventsUnsupervised learning approachLearning-based methodsMachine learning-based methodsEnsemble learning approachMachine learning pipelineEnsemble learningLearning pipelineMatthews correlation coefficientAlgorithmic fairnessReal worldSHapley Additive exPlanations valuesCurrent workflowsClinical workflowWorkflowOperational burdenBasic metabolic panelIntravenous (IVPipelineInternal validation setValidation setFlagging ratesPerformance assessmentDifferences Between Serum and Plasma: An Indirect Approach to Derive a Combined Reference Interval for Total Protein, Albumin, and Globulin
Militello L, El-Khoury J, Durant T. Differences Between Serum and Plasma: An Indirect Approach to Derive a Combined Reference Interval for Total Protein, Albumin, and Globulin. American Journal Of Clinical Pathology 2024, 162: s170-s170. DOI: 10.1093/ajcp/aqae129.373.Peer-Reviewed Original ResearchComprehensive metabolic panelSerum separator tubesReference intervalsHealthy individualsClinically significant differencesLithium heparinLaboratory information systemSerum samplesSerum globulinDerivation of reference intervalsRetrospective patient dataTotal proteinMetabolic panelPatient populationInpatient statusExclusion criteriaAccounting for differences between serum and plasma: An indirect approach to derive reference intervals for total protein, albumin, and globulin
Militello L, El-Khoury J, Durant T. Accounting for differences between serum and plasma: An indirect approach to derive reference intervals for total protein, albumin, and globulin. Clinica Chimica Acta 2024, 562: 119851. PMID: 38977172, DOI: 10.1016/j.cca.2024.119851.Peer-Reviewed Original Research2024 ICBE-B: AI/Predictive Model Verification
Durant T, Seheult JN; College of American Pathologists (CAP) Informatics Committee. 2024 ICBE-B: AI/Predictive Model Verification. Northfield, IL: CAP; 2024.Peer-Reviewed Educational MaterialsBiomarkers vs Machines: The Race to Predict Acute Kidney Injury
Ghazi L, Farhat K, Hoenig M, Durant T, El-Khoury J. Biomarkers vs Machines: The Race to Predict Acute Kidney Injury. Clinical Chemistry 2024, 70: 805-819. PMID: 38299927, DOI: 10.1093/clinchem/hvad217.Peer-Reviewed Original ResearchAcute kidney injuryEarly diagnosisInsulin-like growth factor-binding protein 7Prediction of acute kidney injuryGrowth factor-binding protein 7Detect acute kidney injuryNeutrophil gelatinase-associated lipocalinAcute kidney injury detectionClinical practiceIrreversible kidney damageClinical outcome studiesImminent AKISerum creatinineKidney injuryInjury markersPediatric populationCystatin CTissue inhibitorHospitalized patientsClinical useKidney damageRegulatory approvalProtein 7BiomarkersOutcome studies
2023
Retrospective evaluation of clinical decision support for within-laboratory optimization of SARS-CoV-2 NAAT workflow
Durant T, Peaper D. Retrospective evaluation of clinical decision support for within-laboratory optimization of SARS-CoV-2 NAAT workflow. Journal Of Clinical Microbiology 2023, 62: e00785-23. PMID: 38132702, PMCID: PMC10865785, DOI: 10.1128/jcm.00785-23.Peer-Reviewed Original ResearchData Analytics in Clinical Laboratories: Advancing Diagnostic Medicine in the Digital Age
Merrill A, Durant T, Baron J, Klutts J, Obstfeld A, Peaper D, Stoffel M, Wheeler S, Zaydman M. Data Analytics in Clinical Laboratories: Advancing Diagnostic Medicine in the Digital Age. Clinical Chemistry 2023, 69: 1333-1341. PMID: 37962514, DOI: 10.1093/clinchem/hvad183.Commentaries, Editorials and Letters