Thomas Durant, MD
Associate Professor of Laboratory MedicineCards
About
Titles
Associate Professor of Laboratory Medicine
Medical Director, Chemical Pathology, Laboratory Medicine; Medical Director, Laboratory Informatics, Laboratory Medicine; Associate Director, ACGME Chemical Pathology Fellowship, Laboratory Medicine; Medical Director, Immunology, Laboratory Medicine
Biography
Dr. Thomas Durant is an Associate Professor of Laboratory Medicine and Biomedical Informatics and Data Science at the Yale School of Medicine. He is the Medical Director of Chemical Pathology, Clinical Immunology, 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
- All Institutions
- Biomedical Informatics & Data Science
- Clinical Chemistry Laboratory
- Clinical Laboratory Informatics
- Laboratory Medicine
- Yale Medicine
- Yale New Haven Health System
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, PMCID: PMC12928752, 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
2026
Intraoperative tissue aspirate testing: A single-center experience and evaluation of criteria for parathyroid tissue confirmation
Kodger J, Merchant N, El-Khoury J, Ogilvie J, Ramirez A, Durant T. Intraoperative tissue aspirate testing: A single-center experience and evaluation of criteria for parathyroid tissue confirmation. Clinica Chimica Acta 2026, 588: 120981. PMID: 41876081, DOI: 10.1016/j.cca.2026.120981.Peer-Reviewed Original ResearchParathyroid tissueBiochemical cureParathyroid hormoneDiagnostic performanceIntraoperative measurement of parathyroid hormoneIntraoperative measurementsMeasurement of parathyroid hormoneBiochemical cure ratesRemoval of parathyroid tissueSingle-center experienceParathyroid hormone measurementTissue confirmationPrimary hyperparathyroidismAdenoma casesHistopathological diagnosisSerum calciumPTH ratioCure rateDiagnostic accuracyAdjunctive techniquesPatientsExcised tissueClinical practiceParathyroidectomySix-monthsValidating CALIPER pediatric reference intervals in a U.S. population using retrospective outpatient data and RefineR
Kodger J, Durant TJ, Yurtsever N, El-Khoury JM. Validating CALIPER pediatric reference intervals in a U.S. population using retrospective outpatient data and RefineR. Clinica Chimica Acta 2026, 584: 120846. DOI: 10.1016/j.cca.2026.120846.Peer-Reviewed Original ResearchQuantum Machine Learning and Data Re-Uploading: Evaluation on Benchmark and Laboratory Medicine Data Sets
Durant TJS, Lee SJ, Dudgeon SN, Knight E, Nelson B, Young HP, Ohno-Machado L, Schulz WL. Quantum Machine Learning and Data Re-Uploading: Evaluation on Benchmark and Laboratory Medicine Data Sets. Clinical Chemistry 2026, hvaf192. DOI: 10.1093/clinchem/hvaf192.Peer-Reviewed Original ResearchQuantum Machine Learning and Data Re-Uploading: Evaluation on Benchmark and Laboratory Medicine Data Sets
Durant T, Lee S, Dudgeon S, Knight E, Nelson B, Young H, Ohno-Machado L, Schulz W. Quantum Machine Learning and Data Re-Uploading: Evaluation on Benchmark and Laboratory Medicine Data Sets. Clinical Chemistry 2026, hvaf192. PMID: 41728802, DOI: 10.1093/clinchem/hvaf192.Peer-Reviewed Original ResearchQuantum machine learningReal-world healthcare dataMachine learningClassification performanceML algorithmsQuantum machine learning methodsComparison of classification performanceData setsBenchmark data setsLow-dimensional dataClassical machine learningInput dimensionalityRe-uploadingF1 scoreF-scoreClassic algorithmConfiguration parametersAlgorithmAlgorithm developmentLinear algorithmQuantum hardwareImpact of optimismQuantum machinesBaseline comparisonLearningValidating CALIPER pediatric reference intervals in a U.S. population using retrospective outpatient data and RefineR
Kodger J, Durant T, Yurtsever N, El-Khoury J. Validating CALIPER pediatric reference intervals in a U.S. population using retrospective outpatient data and RefineR. Clinica Chimica Acta 2026, 584: 120846. PMID: 41565092, DOI: 10.1016/j.cca.2026.120846.Peer-Reviewed Original ResearchConceptsPediatric reference intervalsBody mass indexCALIPER reference intervalsCLSI guidelinesReference intervalsCanadian Laboratory InitiativePediatric laboratory medicinePopulation-specific reference intervalsOutpatient pediatric populationPediatric outpatient populationBlood urea nitrogenYears of ageEP28-A3cPediatric populationMass indexOutpatient dataCLSIU.S. populationAlanine aminotransferaseOutpatient populationInterpretation of laboratory resultsUrea nitrogen
2025
Large language models mediated extraction of clinical information from bone marrow biopsy pathology reports
Lanino L, Getz T, Kewan T, Kiwan A, Rolles B, Bidikian A, Sariipek N, Mendez L, Podoltsev N, Durant T, Meeker D, Perincheri S, Gershkovich P, Katz S, Siddon A, Xu M, Bewersdorf J, Della Porta M, Zeidan A, Stahl M. Large language models mediated extraction of clinical information from bone marrow biopsy pathology reports. Blood 2025, 146: 2555. DOI: 10.1182/blood-2025-2555.Peer-Reviewed Original ResearchMedical record numberElectronic health recordsLanguage modelExtraction accuracyBlast countAutomated extractionBiopsy pathology reportConfidence intervalsExtraction of clinical informationHallucination ratingsPathology reportsRule-based algorithmReal-world dataSystematically extract informationClinical researchCategorical variablesFibrosis gradeZero-ShotUnprocessed textRinged sideroblastsExpert systemHealth recordsContinuous variablesSpearman correlation coefficientCode adjustmentA-056 Ex-Vivo Parathyroid Aspiration in Parathyroidectomy: Correlation with Histopathology and Biochemical Outcomes in Single- and Multi-glandular Primary Hyperparathyroidism
Kodger J, Durant T, Merchant N, Ramirez A. A-056 Ex-Vivo Parathyroid Aspiration in Parathyroidectomy: Correlation with Histopathology and Biochemical Outcomes in Single- and Multi-glandular Primary Hyperparathyroidism. Clinical Chemistry 2025, 71: hvaf086.055. DOI: 10.1093/clinchem/hvaf086.055.Peer-Reviewed Original ResearchMultiglandular diseasePrimary hyperparathyroidismPreoperative diagnosisSurgical managementParathyroid tissuePreoperative diagnosis of primary hyperparathyroidismSurgical management of primary hyperparathyroidismIntraoperative parathyroid hormone testingDiagnosis of primary hyperparathyroidismPresence of parathyroid tissueManagement of primary hyperparathyroidismParathyroid hormone testingTertiary academic medical centerSubgroup of patientsParathyroid aspiratesBiochemical cureBiochemical outcomesHistopathological diagnosisHormone testRetrospective studyHistopathological resultsAcademic medical centerEndocrine disordersIntraoperative testingParathyroidectomy
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