2024
Evaluating Ischemic Heart Disease in Women: Focus on Angina With Nonobstructive Coronary Arteries (ANOCA)
Maayah M, Latif N, Vijay A, Gallegos C, Cigarroa N, Martinez E, Mazure C, Miller E, Spatz E, Shah S. Evaluating Ischemic Heart Disease in Women: Focus on Angina With Nonobstructive Coronary Arteries (ANOCA). Journal Of The Society For Cardiovascular Angiography & Interventions 2024, 3: 102195. PMID: 39166160, PMCID: PMC11330936, DOI: 10.1016/j.jscai.2024.102195.Peer-Reviewed Original ResearchIschemic heart diseaseNonobstructive coronary arteriesCoronary artery diseaseArtery diseaseObstructive coronary artery diseaseCoronary arteryLong-term cardiovascular prognosisHeart diseaseSuspected ischemic heart diseaseAtherosclerotic coronary artery diseaseCoronary vasomotor disordersPretest probability of diseasePragmatic diagnostic algorithmExpert consensus documentEvaluating ischemic heart diseaseImpact quality of lifeQuality of lifeSymptomatic patientsPretest probabilityANOCACardiovascular prognosisVasomotor disordersDiagnostic evaluationPersistent symptomsDiagnostic strategiesTAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Guo X, Shi L, Chen X, Liu Q, Zhou B, Xie H, Liu Y, Palyo R, Miller E, Sinusas A, Staib L, Spottiswoode B, Liu C, Dvornek N. TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction. Medical Image Analysis 2024, 96: 103190. PMID: 38820677, PMCID: PMC11180595, DOI: 10.1016/j.media.2024.103190.Peer-Reviewed Original ResearchGenerative adversarial networkAdversarial networkMotion estimation accuracyInter-frame motionIntensity-based image registration techniqueAll-to-oneSegmentation masksImage registration techniquesOriginal frameTemporal informationDiagnosis accuracyMyocardial blood flowEstimation accuracyFrame conversionPositron emission tomographyNovel methodImage qualityPET datasetsRegistration techniqueNetworkCardiac positron emission tomographyBlood flowDynamic cardiac positron emission tomographyMotion correctionCoronary artery diseaseReal-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study
Oikonomou E, Aminorroaya A, Dhingra L, Partridge C, Velazquez E, Desai N, Krumholz H, Miller E, Khera R. Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study. European Heart Journal - Digital Health 2024, 5: 303-313. PMID: 38774380, PMCID: PMC11104476, DOI: 10.1093/ehjdh/ztae023.Peer-Reviewed Original ResearchRisk of acute myocardial infarctionAssociated with lower oddsHospital health systemCoronary artery diseaseCardiac testingRisk of adverse outcomesUK BiobankHealth systemProvider-drivenLower oddsAssociated with better outcomesAcute myocardial infarctionBlack raceStable chest painFemale sexReal world evaluationDiabetes historyMulticohort studyFunction testsSuspected coronary artery diseaseYounger ageRisk profileAdverse outcomesMultinational cohortPost hoc analysisAI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging
Miller R, Shanbhag A, Killekar A, Lemley M, Bednarski B, Kavanagh P, Feher A, Miller E, Bateman T, Builoff V, Liang J, Newby D, Dey D, Berman D, Slomka P. AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging. JACC Cardiovascular Imaging 2024, 17: 780-791. PMID: 38456877, PMCID: PMC11222053, DOI: 10.1016/j.jcmg.2024.01.006.Peer-Reviewed Original ResearchComputed tomography attenuation correctionMajor adverse cardiovascular eventsCoronary artery calciumLV massMyocardial perfusion imagingPerfusion imagingSingle-photon emission computed tomography/computed tomographyIncreased risk of MACEAssociated with major adverse cardiovascular eventsRisk of Major Adverse Cardiovascular EventsCardiac chamber volumesMedian follow-upCT-based volumesRight ventricular volumesImprove cardiovascular risk assessmentAdverse cardiovascular eventsQuartile of LV massContinuous net reclassification indexCoronary artery diseaseNet reclassification indexCardiovascular risk assessmentTomography/computed tomographyConsecutive patientsImprove risk predictionPrognostic utilityImpact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography
Randazzo M, Elias P, Poterucha T, Sharir T, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Castillo M, Liang J, Miller R, Dey D, Berman D, Slomka P, Einstein A. Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography. European Heart Journal - Cardiovascular Imaging 2024, 25: 996-1006. PMID: 38445511, PMCID: PMC11210974, DOI: 10.1093/ehjci/jeae055.Peer-Reviewed Original ResearchSPECT myocardial perfusion imagingMyocardial perfusion imagingCoronary artery diseaseCardiac sizeDetect obstructive coronary artery diseasePerfusion imagingObstructive coronary artery diseaseDetection of coronary artery diseaseNegative predictive valueLeft ventricular volumeSolid-state scannersSmall cardiac sizeCoronary angiographyMale patientsYounger patientsElderly patientsMale sexDiagnostic performanceVentricular volumeArtery diseaseVisual assessmentPatientsPredictive valueElderly ageSPECTAI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging
Miller R, Shanbhag A, Killekar A, Lemley M, Bednarski B, Van Kriekinge S, Kavanagh P, Feher A, Miller E, Einstein A, Ruddy T, Liang J, Builoff V, Berman D, Dey D, Slomka P. AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging. Npj Digital Medicine 2024, 7: 24. PMID: 38310123, PMCID: PMC10838293, DOI: 10.1038/s41746-024-01020-z.Peer-Reviewed Original ResearchEpicardial adipose tissueMyocardial infarctionPerfusion imagingEpicardial adipose tissue measurementsEpicardial adipose tissue volumeEAT attenuationMedian follow-upIncreased risk of deathEpicardial fat measurementMyocardial perfusion imagingAssociated with cardiovascular riskCoronary artery diseaseAssociated with deathEating measuresRisk of deathEAT volumeLow-dosePrognostic insightsFollow-upCardiovascular riskCardiovascular risk predictionUngated CTArtery diseaseIncreased riskCardiac silhouetteClinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study
Miller R, Bednarski B, Pieszko K, Kwiecinski J, Williams M, Shanbhag A, Liang J, Huang C, Sharir T, Hauser M, Dorbala S, Di Carli M, Fish M, Ruddy T, Bateman T, Einstein A, Kaufmann P, Miller E, Sinusas A, Acampa W, Han D, Dey D, Berman D, Slomka P. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study. EBioMedicine 2024, 99: 104930. PMID: 38168587, PMCID: PMC10794922, DOI: 10.1016/j.ebiom.2023.104930.Peer-Reviewed Original ResearchMyocardial infarctionMyocardial perfusion imagingBlood InstituteNational HeartPharmacologic stressExternal testing cohortNormal imaging resultsRetrospective observational studyCoronary artery diseasePrevious myocardial infarctionRisk of deathNormal perfusion scanBritish Heart FoundationNational InstituteCluster 4 patientsDistinct phenotypesCardiovascular riskArtery diseaseRisk stratificationPerfusion scanNormal perfusionImaging featuresNormal scansMPI patientsHeart Foundation
2023
Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging
Williams M, Bednarski B, Pieszko K, Miller R, Kwiecinski J, Shanbhag A, Liang J, Huang C, Sharir T, Dorbala S, Di Carli M, Einstein A, Sinusas A, Miller E, Bateman T, Fish M, Ruddy T, Acampa W, Hauser M, Kaufmann P, Dey D, Berman D, Slomka P. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging. European Journal Of Nuclear Medicine And Molecular Imaging 2023, 50: 2656-2668. PMID: 37067586, PMCID: PMC10317876, DOI: 10.1007/s00259-023-06218-z.Peer-Reviewed Original ResearchConceptsCoronary artery diseaseMyocardial perfusion imagingCause mortalityTotal perfusion deficitArtery diseaseRisk stratificationPerfusion deficitsHigher body mass indexTomography myocardial perfusion imagingMore diabetes mellitusSPECT myocardial perfusion imagingBetter risk stratificationRisk-stratify patientsBody mass indexREFINE SPECT registrySingle photon emissionDiabetes mellitusMass indexExternal cohortStress imagingMyocardial perfusionPatientsRisk phenotypePerfusion imagingImaging characteristics
2022
Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images
Miller RJH, Singh A, Otaki Y, Tamarappoo BK, Kavanagh P, Parekh T, Hu LH, Gransar H, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli MF, Liang JX, Dey D, Berman DS, Slomka PJ. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 50: 387-397. PMID: 36194270, PMCID: PMC10042590, DOI: 10.1007/s00259-022-05972-w.Peer-Reviewed Original ResearchConceptsCoronary artery diseaseMyocardial perfusion imagingArtery diseaseInvasive angiographyObstructive coronary artery diseaseDisease probabilityLow-risk patientsLow-risk populationHigh-risk populationTotal perfusion deficitHigh diagnostic accuracyS-TPDPerfusion deficitsPatient managementPatientsPerfusion imagingDiagnostic accuracyPerfusion SPECT imagesLower likelihoodGood calibrationCharacteristic curveAngiographySPECT imagesSelection biasDiseaseUnsupervised machine learning improves risk stratification of patients with visual normal SPECT myocardial perfusion imaging assessments
Bednarski B, Williams M, Pieszko K, Miller R, Huang C, Kwiecinski J, Sharir T, Di Carli M, Fish M, Ruddy T, Hasuer T, Miller E, Acampa W, Berman D, Slomka P. Unsupervised machine learning improves risk stratification of patients with visual normal SPECT myocardial perfusion imaging assessments. European Heart Journal 2022, 43: ehac544.300. DOI: 10.1093/eurheartj/ehac544.300.Peer-Reviewed Original ResearchMajor adverse cardiac eventsPeak systolic blood pressureSystolic blood pressureHigh-risk clustersRisk stratificationMyocardial perfusion imagingNormal perfusionHazard ratioBlood pressureCox proportional hazards analysisHigher left ventricular massHigher body mass indexTomography myocardial perfusion imagingNormal clinical assessmentRisk-stratified subgroupsAdverse cardiac eventsPrevalence of diabetesProportional hazards analysisBetter risk stratificationCoronary artery diseaseImproved risk stratificationManagement of patientsKaplan-Meier curvesBody mass indexLeft ventricular massThe Prognostic Value of CAC Zero Among Individuals Presenting With Chest Pain A Meta-Analysis
Agha AM, Pacor J, Grandhi GR, Mszar R, Khan SU, Parikh R, Agrawal T, Burt J, Blankstein R, Blaha MJ, Shaw LJ, Al-Mallah MH, Brackett A, Cainzos-Achirica M, Miller EJ, Nasir K. The Prognostic Value of CAC Zero Among Individuals Presenting With Chest Pain A Meta-Analysis. JACC Cardiovascular Imaging 2022, 15: 1745-1757. PMID: 36202453, DOI: 10.1016/j.jcmg.2022.03.031.Peer-Reviewed Original ResearchConceptsObstructive coronary artery diseaseAcute chest painCoronary artery calciumCoronary artery diseaseStable chest painNonobstructive coronary artery diseaseMajor adverse cardiac eventsAdverse cardiac eventsChest painCardiac eventsLow prevalenceAbsence of CACHealth care delivery modelsCare delivery modelsA Meta-AnalysisNegative predictive valueCAC assessmentArtery calciumCAC scoreIntermediate riskArtery diseaseCoronary CTACTA assessmentPrognostic valueTomography angiographyExplainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.
Miller RJH, Kuronuma K, Singh A, Otaki Y, Hayes S, Chareonthaitawee P, Kavanagh P, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Carli MD, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging. Journal Of Nuclear Medicine 2022, 63: 1768-1774. PMID: 35512997, PMCID: PMC9635672, DOI: 10.2967/jnumed.121.263686.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingCoronary artery diseaseObstructive coronary artery diseasePhysician interpretationDiagnostic accuracyPerfusion imagingLeft main arteryOverall net reclassification improvementInvasive coronary angiographyNet reclassification improvementTotal perfusion deficitArtery diseaseCoronary angiographyMedian agePhysician diagnosisReclassification improvementPerfusion deficitsClinical historyCoronary segmentsRepresentative cohortMeaningful improvementsMain arteryPatientsDL resultsQuantitative perfusion
2021
Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry
Han D, Rozanski A, Gransar H, Tzolos E, Miller RJH, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Hu LH, Dey D, Berman DS, Slomka PJ. Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry. Journal Of Nuclear Cardiology 2021, 29: 3003-3014. PMID: 34757571, PMCID: PMC9085969, DOI: 10.1007/s12350-021-02810-8.Peer-Reviewed Original ResearchConceptsMajor adverse cardiovascular eventsCoronary artery diseaseTotal perfusion deficitCardiac stress testingStress testingComparison of diabetesAdverse cardiovascular eventsStress test patientsCardiac stress test patientsREFINE SPECT registryDM statusMACE riskBackgroundDiabetes mellitusCardiovascular eventsArtery diseaseVentricular functionPrognostic predictorClinical variablesPerfusion deficitsChi-square analysisSPECT-MPIPatientsTest patientsPropensity scorePotent predictorImpact of age, sex, and cardiac size on the diagnostic performance of myocardial perfusion single-photon emission computed tomography: insights from the REFINE SPECT registry
Randazzo M, Elias P, Poterucha T, Sharir T, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Berman D, Slomka P, Einstein A. Impact of age, sex, and cardiac size on the diagnostic performance of myocardial perfusion single-photon emission computed tomography: insights from the REFINE SPECT registry. European Heart Journal 2021, 42: ehab724.0254. DOI: 10.1093/eurheartj/ehab724.0254.Peer-Reviewed Original ResearchCoronary artery diseaseElderly patientsImpact of ageSingle photon emissionDiagnostic performanceYounger patientsFemale patientsCardiac sizeSPECT-MPILower EDVCardiac volumesObstructive coronary artery diseasePrediction of CADTomography myocardial perfusion imagingMyocardial perfusion single-photon emissionDiagnostic accuracyPerfusion single-photon emissionCardiac event rateStress total perfusion deficitReceiver-operating characteristic curveInvasive coronary angiographyEnd-diastolic volumeCardiac chamber sizeAlternative diagnostic modalityTotal perfusion deficitClinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease
Otaki Y, Singh A, Kavanagh P, Miller RJH, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease. JACC Cardiovascular Imaging 2021, 15: 1091-1102. PMID: 34274267, PMCID: PMC9020794, DOI: 10.1016/j.jcmg.2021.04.030.Peer-Reviewed Original ResearchConceptsTotal perfusion deficitMyocardial perfusion imagingStandard clinical softwareStress total perfusion deficitObstructive CADTomography myocardial perfusion imagingSPECT myocardial perfusion imagingStress myocardial perfusionCoronary artery diseaseReceiver-operating characteristic curveInvasive coronary angiographyClinical softwareReader diagnosisSingle photon emissionTypical clinical workflowArtery diseaseCoronary angiographyPerfusion deficitsDiagnostic findingsMyocardial perfusionPerfusion imagingVentricular volumeStandard clinical workstationPatientsDiagnostic accuracyDiagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT
Eisenberg E, Miller RJH, Hu LH, Rios R, Betancur J, Azadani P, Han D, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT. Journal Of Nuclear Cardiology 2021, 29: 2295-2307. PMID: 34228341, PMCID: PMC9020793, DOI: 10.1007/s12350-021-02698-4.Peer-Reviewed Original ResearchConceptsObstructive coronary artery diseaseCoronary artery diseaseHigh-risk coronary artery diseaseMyocardial perfusion imagingPatient selection algorithmTriple-vessel coronary artery diseasePrediction of CADML thresholdsStress-first protocolInvasive coronary angiographyReceiver operator characteristic curveOperator characteristic curveMyocardial perfusion SPECTArtery diseaseCoronary angiographyAnterior descendingClinical variablesClinical algorithmReader diagnosisRest imagingPerfusion SPECTPerfusion imagingDiagnostic safetyRadiation doseCharacteristic curveA phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST)
Oikonomou EK, Van Dijk D, Parise H, Suchard MA, de Lemos J, Antoniades C, Velazquez EJ, Miller EJ, Khera R. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). European Heart Journal 2021, 42: 2536-2548. PMID: 33881513, PMCID: PMC8488385, DOI: 10.1093/eurheartj/ehab223.Peer-Reviewed Original ResearchConceptsStable chest painChest painPrimary endpointMajor adverse cardiovascular eventsNon-fatal myocardial infarctionAdverse cardiovascular eventsStudy's primary endpointCoronary artery diseaseClinical trial populationsCox regression modelParticipant-level dataSCOT-HEARTCardiovascular eventsCause mortalityHazard ratioPatients 5Artery diseaseFunctional testingPROMISE trialTrial populationMyocardial infarctionLower incidenceStudy populationPainCollected variablesDiagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning
Liu H, Wu J, Miller EJ, Liu C, Yaqiang, Liu, Liu YH. Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning. European Journal Of Nuclear Medicine And Molecular Imaging 2021, 48: 2793-2800. PMID: 33511425, DOI: 10.1007/s00259-021-05202-9.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingCoronary artery diseaseMyocardial perfusion abnormalitiesPerfusion abnormalitiesDiagnostic accuracyConvolutional neural networkTomography myocardial perfusion imagingYale-New Haven HospitalMyocardial perfusion defect sizeSPECT myocardial perfusion imagingAbnormal myocardial perfusionReceiver-operating characteristic curvePerfusion defect sizeNew Haven HospitalAUC valuesSingle photon emissionMyocardial perfusion SPECTDeep learningHigh diagnostic accuracyArtery diseaseDL methodsFinal diagnosisPatient genderMyocardial perfusionPerfusion SPECT
2020
PROGNOSTIC IMPACT OF DIABETES IN PATIENTS WITH OR WITHOUT KNOWN CORONARY ARTERY DISEASE, RESULTS FROM THE REFINE SPECT REGISTRY
Han D, Otaki Y, Tzolos E, Klein E, Gransar H, Sharir T, Einstein A, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Liang J, Dey D, Berman D, Slomka P. PROGNOSTIC IMPACT OF DIABETES IN PATIENTS WITH OR WITHOUT KNOWN CORONARY ARTERY DISEASE, RESULTS FROM THE REFINE SPECT REGISTRY. Journal Of The American College Of Cardiology 2020, 75: 1796. DOI: 10.1016/s0735-1097(20)32423-2.Peer-Reviewed Original Research
2019
Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: A report from REFINE SPECT registry
Hu LH, Sharir T, Miller RJH, Einstein AJ, Fish MB, Ruddy TD, Dorbala S, Di Carli M, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Betancur J, Germano G, Liang JX, Commandeur F, Azadani PN, Gransar H, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: A report from REFINE SPECT registry. Journal Of Nuclear Cardiology 2019, 27: 1180-1189. PMID: 31087268, PMCID: PMC6851400, DOI: 10.1007/s12350-019-01730-y.Peer-Reviewed Original ResearchConceptsTransient ischemic dilationUpper reference limitReference limitsMulticenter registrySupine positionTransient ischemic dilation ratioCoronary artery diseaseCadmium zinc telluride cameraQuantitative perfusion SPECT softwareREFINE SPECT registryIschemic dilationArtery diseasePerfusion findingsMyocardial perfusionD-SPECTRegistryLower likelihoodSPECT softwareTelluride cameraRadiotracerDilation ratioDifferent protocolsPatientsPerfusionDisease