Cristiana Baloescu, MD, MPH
Assistant Professor of Emergency MedicineCards
About
Research
Overview
Current research projects include investigating software applications providing a quantitative assessment of interstitial fluid on lung point-of-care ultrasound and whether this corresponds to degree of clinical disease severity, automated estimation of ejection fraction on point-of-care echo clips.
I am also currently developing research studies in point-of-care ultrasound guided nerve blocks.
If you are a medical student looking for a research project to get involved with, please email me for details.
Publications
2025
Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study
Oikonomou E, Vaid A, Holste G, Coppi A, McNamara R, Baloescu C, Krumholz H, Wang Z, Apakama D, Nadkarni G, Khera R. Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. The Lancet Digital Health 2025, 7: e113-e123. PMID: 39890242, DOI: 10.1016/s2589-7500(24)00249-8.Peer-Reviewed Original ResearchConceptsYale New Haven Health SystemPoint-of-care ultrasonographyMount Sinai Health SystemTransthyretin amyloid cardiomyopathyArtificial intelligenceHealth systemAmyloid cardiomyopathyHypertrophic cardiomyopathyRetrospective cohort of individualsCardiomyopathy casesTesting artificial intelligenceConvolutional neural networkSinai Health SystemCohort of individualsOpportunistic screeningHypertrophic cardiomyopathy casesMulti-labelPositive screenAI frameworkEmergency departmentMortality riskNeural networkLoss functionCardiac ultrasonographyAugmentation approachArtificial Intelligence-Guided Lung Ultrasound by Nonexperts.
Baloescu C, Bailitz J, Cheema B, Agarwala R, Jankowski M, Eke O, Liu R, Nomura J, Stolz L, Gargani L, Alkan E, Wellman T, Parajuli N, Marra A, Thomas Y, Patel D, Schraft E, O'Brien J, Moore CL, Gottlieb M. Artificial Intelligence-Guided Lung Ultrasound by Nonexperts. JAMA Cardiol 2025 PMID: 39813064, DOI: 10.1001/jamacardio.2024.4991.Peer-Reviewed Original ResearchThis study shows AI helps non-experts create expert-quality lung ultrasound images, which may improve healthcare diagnostics access in underserved areas.
2024
Construction and performance of a clinical prediction rule for ureteral stone without the use of race or ethnicity: A new STONE score
Moore C, Gross C, Hart L, Molinaro A, Rhodes D, Singh D, Baloescu C. Construction and performance of a clinical prediction rule for ureteral stone without the use of race or ethnicity: A new STONE score. Journal Of The American College Of Emergency Physicians Open 2024, 5: e13324. PMID: 39524039, PMCID: PMC11543628, DOI: 10.1002/emp2.13324.Peer-Reviewed Original ResearchClinical prediction ruleArea under the receiver operating characteristic curveSTONE scoreMultivariate logistic regressionUreteral stonesComputed tomographyPrediction ruleUncomplicated renal colicKidney stonesReceiver operating characteristic curveLogistic regressionNon-black raceDiagnosis of kidney stonesGross hematuriaMicroscopic hematuriaRenal colicPotential adverse effectsDiagnostic accuracyHematuriaClinical algorithmMale genderProspective dataClinical accuracyRetrospective dataCharacteristic curveDeep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure
Baloescu C, Chen A, Varasteh A, Hall J, Toporek G, Patil S, McNamara R, Raju B, Moore C. Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure. The Ultrasound Journal 2024, 16: 42. PMID: 39283362, PMCID: PMC11405569, DOI: 10.1186/s13089-024-00391-4.Peer-Reviewed Original ResearchB-line scoreComposite congestion scoreCongestion scoreHeart failureSeverity scoreB-linesCongestive heart failurePatients suspectedPulmonary congestionLung zonesClinical progressionRothman IndexClinical assessmentDisease severityPatientsPresence of artifactsEvaluate changesLungDetect fluidUltrasound experienceMixed effects modelsScoresDiseaseInterstitial spaceUltrasound systemPatient factors associated with unplanned sedation after intra-articular lidocaine for shoulder dislocation
Wright D, Sherak R, Seo L, Parhar A, Baloescu C. Patient factors associated with unplanned sedation after intra-articular lidocaine for shoulder dislocation. JEM Reports 2024, 3: 100093. DOI: 10.1016/j.jemrpt.2024.100093.Peer-Reviewed Original ResearchIntra-articular lidocaineIV sedationAdverse eventsShoulder dislocationRate of procedural complicationsRetrospective observational cohort studyQuantity of opioidsAcute anterior shoulder dislocationED length of stayAnterior shoulder dislocationObservational cohort studyPatient-level factorsMultivariate logistic regressionShoulder dislocation reductionAcademic hospital systemLength of stayAssociated with greater ratesComplication rateProcedural complicationsAdult patientsProspective studyUnivariate analysisIntravenous sedationCohort studyED lengthSpatiotemporal Deep Learning-Based Cine Loop Quality Filter for Handheld Point-of-Care Echocardiography
Mukaddim R, Mackay E, Gessert N, Erkamp R, Sethuraman S, Sutton J, Bharat S, Jutras M, Baloescu C, Moore C, Raju B. Spatiotemporal Deep Learning-Based Cine Loop Quality Filter for Handheld Point-of-Care Echocardiography. IEEE Transactions On Ultrasonics Ferroelectrics And Frequency Control 2024, 71: 1577-1587. PMID: 38700961, DOI: 10.1109/tuffc.2024.3396796.Peer-Reviewed Original ResearchOptical flow framesHigh-quality framesLow-quality framesNeural network architectureDeep learning modelsInput framesFrame levelEcho framesNetwork architectureSpatiotemporal deep learning modelCNN modelTemporal informationLV borderLearning modelsTest datasetSpatial informationFlow frameCNNImage qualityPoint-of-careQuantification algorithmHandheldAutomated quantification algorithmImage artifactsImage interpretationEmergency Physician-performed Echocardiogram in Non-ST Elevation Acute Coronary Syndrome Patients Requiring Coronary Intervention
Tan T, Wright D, Baloescu C, Lee S, Moore C. Emergency Physician-performed Echocardiogram in Non-ST Elevation Acute Coronary Syndrome Patients Requiring Coronary Intervention. Western Journal Of Emergency Medicine 2024, 0: 9-16. PMID: 38205979, PMCID: PMC10777186, DOI: 10.5811/westjem.60508.Peer-Reviewed Original ResearchConceptsPresence of RWMARegional wall motion abnormalitiesCoronary interventionEmergency departmentEmergency physiciansElevation acute coronary syndrome patientsNew regional wall motion abnormalitiesNon-ST elevation acute coronary syndrome patientsAcute coronary syndrome patientsRetrospective observational cohort studyEarly coronary interventionDiagnostic coronary angiographyCoronary artery bypassCoronary syndrome patientsObservational cohort studyActivation criteriaIdentification of patientsWall motion abnormalitiesCare transthoracic echocardiographyMeeting inclusion criteriaED electrocardiogramArtery bypassSame admissionAdult patientsCohort study
2023
Development and interobserver reliability of a rating scale for lung ultrasound pathology in lower respiratory tract infection
Baloescu C, Chen A, Schnittke N, Hicks B, Zhu M, Kaili M, Shupp J, Chan D, Malia L, Coneybeare D, Gregory K, Kessler D, Raju B, Moore C. Development and interobserver reliability of a rating scale for lung ultrasound pathology in lower respiratory tract infection. WFUMB Ultrasound Open 2023, 1: 100006. DOI: 10.1016/j.wfumbo.2023.100006.Peer-Reviewed Original ResearchLower respiratory tract infectionsRespiratory tract infectionsIntraclass correlation coefficientTract infectionsUltrasound pathologiesRating ScaleSonographic findingsInter-rater reliabilityExpert consensusPleural line abnormalitiesGood inter-rater reliabilityAverage intraclass correlation coefficientSeverity Rating ScalePleural effusionLung aerationSeverity ScaleFourth subsetInterobserver reliabilityCine clipsInfectionUltrasound expertsScanning protocolPathologyB-linesCOVID-19Machine Learning Algorithm Detection of Confluent B-Lines
Baloescu C, Rucki A, Chen A, Zahiri M, Ghoshal G, Wang J, Chew R, Kessler D, Chan D, Hicks B, Schnittke N, Shupp J, Gregory K, Raju B, Moore C. Machine Learning Algorithm Detection of Confluent B-Lines. Ultrasound In Medicine & Biology 2023, 49: 2095-2102. PMID: 37365065, DOI: 10.1016/j.ultrasmedbio.2023.05.016.Peer-Reviewed Original ResearchTwo‐ Versus 8‐Zone Lung Ultrasound in Heart Failure: Analysis of a Large Data Set Using a Deep Learning Algorithm
Baloescu C, Chen A, Varasteh A, Toporek G, McNamara R, Raju B, Moore C. Two‐ Versus 8‐Zone Lung Ultrasound in Heart Failure: Analysis of a Large Data Set Using a Deep Learning Algorithm. Journal Of Ultrasound In Medicine 2023, 42: 2349-2356. PMID: 37255051, DOI: 10.1002/jum.16262.Peer-Reviewed Original ResearchConceptsHeart failureLung ultrasoundBland-Altman plot analysisSubgroup analysisUltrasound protocolMore lung zonesProspective observational studyReal-world clinical useB-linesAdult patientsLung zonesObservational studyClinical useScanning protocolVideo loopsPatientsPlot analysisRate severityUltrasoundAverage severitySeverityScoresTwo- VersusFailureSeverity information
Clinical Care
Overview
Clinical Specialties
Emergency Medicine
Board Certifications
Emergency Medicine
- Certification Organization
- AB of Emergency Medicine
- Original Certification Date
- 2018