Thomas Durant, MD
Associate Professor of Laboratory MedicineCards
About
Titles
Associate Professor of Laboratory Medicine
Medical Director, Chemical Pathology, Laboratory Medicine; Medical Director, Laboratory IT Services, Laboratory Medicine; Associate Director, ACGME Chemical Pathology Fellowship, Laboratory Medicine
Biography
Dr. Thomas Durant is an Assistant Professor of Laboratory Medicine and Biomedical Informatics and Data Science at the Yale School of Medicine. He is the Medical Director of Chemical Pathology and Laboratory Informatics at Yale-New Haven Hospital and the Associate Director for the ACGME Chemical Pathology Fellowship. Dr. Durant's research embodies a practical approach, concentrating on quality care initiatives, laboratory outcomes, statistics, machine learning, and artificial intelligence applications in pathology and clinical laboratory medicine. His primary research interests lie in clinical informatics and the innovative use of data management technology to extract valuable insights into laboratory quality and overall operations for enhanced patient care. Among his ongoing projects are investigations into stream processing of interface data for automated sample identification for subsequent biobanking, as well as machine learning endeavors such as the utilization of 'very deep' convolutional neural networks for the automated classification of digital images obtained in clinical laboratories, graph neural networks, and quantum machine learning.
Appointments
Laboratory Medicine
Associate Professor on TermPrimaryBiomedical Informatics & Data Science
Associate Professor on TermSecondary
Other Departments & Organizations
Education & Training
- Winchester Clinical Microbiology Fellow
- Yale-New Haven Hospital (2019)
- Resident
- Yale-New Haven Hospital (2018)
- Chief Resident
- Yale-New Haven Hospital (2018)
- MD
- University of Connecticut, School of Medicine (2015)
- MA
- Quinnipiac University, Physical Therapy
- BA
- Quinnipiac University, Health Sciences (2008)
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 Original ResearchApplications 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
2025
Re-evaluating the threshold for low total testosterone
Arun AS, Durant TJS, El-Khoury JM, Krumholz HM. Reevaluating the Threshold for Low Total Testosterone. Clinical Chemistry. Published online April 16, 2025:hvaf025. doi:10.1093/clinchem/hvaf025Commentaries, Editorials and LettersAutomated Quality Assurance Rules for Liquid Chromatography–Mass Spectrometry Testing in a Clinical Laboratory
Cassella-Mclane G, McGowan M, Kodger J, Durant TJS. Automated Quality Assurance Rules for Liquid Chromatography–Mass Spectrometry Testing in a Clinical Laboratory. Clinics in Laboratory Medicine. 2025;0(0). doi:10.1016/j.cll.2025.01.006Peer-Reviewed Educational Materials
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, 71: 296-306. 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 assessmentAccounting 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 Reviews, Practice Guidelines, Standards, and Consensus StatementsAcute 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 Research
News
News
Get In Touch
Contacts
Laboratory Medicine
55 Park Street
New Haven, CT 06510
United States