Deep learning-based epicardial adipose tissue measurement, maximizing prognostic information from attenuation correction imaging
Shanbhag A, Miller R, Killekar A, Lemley M, Bednarski B, Van Kriekinge S, Kavanagh P, Feher A, Miller E, Bateman T, Liang J, Builoff V, Berman D, Dey D, Slomka P. Deep learning-based epicardial adipose tissue measurement, maximizing prognostic information from attenuation correction imaging. Progress In Biomedical Optics And Imaging 2024, 12930: 129300b-129300b-6. DOI: 10.1117/12.3007914.Peer-Reviewed Original ResearchEpicardial adipose tissueUngated CTMyocardial infarctionEAT volumeRisk stratificationComputed tomographyEAT volume measurementEpicardial adipose tissue measurementsEpicardial adipose tissue volumeAssociated with risk of cardiovascular eventsRisk of cardiovascular eventsMedian follow-upIncreased risk of deathGated computed tomographyImprove risk stratificationAdipose tissue measurementsAssociated with riskRisk of deathAttenuation correction imagesGated CTChest CTNo significant differencePrognostic informationCardiovascular eventsFollow-upAI-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