2024
Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke
Sommer J, Dierksen F, Zeevi T, Tran A, Avery E, Mak A, Malhotra A, Matouk C, Falcone G, Torres-Lopez V, Aneja S, Duncan J, Sansing L, Sheth K, Payabvash S. Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke. Frontiers In Artificial Intelligence 2024, 7: 1369702. PMID: 39149161, PMCID: PMC11324606, DOI: 10.3389/frai.2024.1369702.Peer-Reviewed Original ResearchEnd-to-endComputed tomography angiographyLarge vessel occlusionConvolutional neural networkDeep learning pipelineTrain separate modelsLogistic regression modelsResNet-50Deep learningAdmission computed tomography angiographyNeural networkLearning pipelineAdmission CT angiographyPreprocessing stepDiagnosis of large vessel occlusionsLarge vessel occlusion strokeReceiver operating characteristic areaEnsemble modelAutomated modelPre-existing morbidityCT angiographyReperfusion successNeurological examCross-validationOcclusion stroke
2013
Hospital Acquired Pneumonia Is Linked to Right Hemispheric Peri-Insular Stroke
Kemmling A, Lev MH, Payabvash S, Betensky RA, Qian J, Masrur S, Schwamm LH. Hospital Acquired Pneumonia Is Linked to Right Hemispheric Peri-Insular Stroke. PLOS ONE 2013, 8: e71141. PMID: 23951094, PMCID: PMC3737185, DOI: 10.1371/journal.pone.0071141.Peer-Reviewed Original ResearchConceptsHemispheric infarctsBrain regionsConsecutive acute stroke patientsConditional logistic regression modelsHospital-Acquired PneumoniaRight hemispheric infarctAcute stroke patientsInsular cortex volumeVolume-based variablesSpecific brain regionsLogistic regression modelsAcquired PneumoniaControl patientsMajor complicationsBrainstem strokeImmune suppressionInfarct sizeStroke patientsAutonomic modulationClinical variablesAdmission imagingInfarct locationUnivariate analysisImmune mechanismsCortex volume
2010
Predicting Language Improvement in Acute Stroke Patients Presenting with Aphasia: A Multivariate Logistic Model Using Location-Weighted Atlas-Based Analysis of Admission CT Perfusion Scans
Payabvash S, Kamalian S, Fung S, Wang Y, Passanese J, Kamalian S, Souza LC, Kemmling A, Harris GJ, Halpern EF, González RG, Furie KL, Lev MH. Predicting Language Improvement in Acute Stroke Patients Presenting with Aphasia: A Multivariate Logistic Model Using Location-Weighted Atlas-Based Analysis of Admission CT Perfusion Scans. American Journal Of Neuroradiology 2010, 31: 1661-1668. PMID: 20488905, PMCID: PMC3640318, DOI: 10.3174/ajnr.a2125.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAphasiaBrainComputer SimulationFemaleHumansLogistic ModelsMaleModels, NeurologicalMultivariate AnalysisPattern Recognition, AutomatedPerfusion ImagingPrognosisRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSensitivity and SpecificityStrokeSubtraction TechniqueTomography, X-Ray ComputedConceptsBrain CTPNIHSS scoreStroke onsetFunctional outcomeFirst-time ischemic strokeProximal cerebral artery occlusionMultiple logistic regression analysisMultivariate logistic regression modelMultivariate modelDischarge NIHSS scoreTotal NIHSS scoreAcute stroke patientsCerebral artery occlusionTime of dischargeCT perfusion imagingLogistic regression analysisMultivariate logistic modelCT perfusion scansLogistic regression modelsAdmission CTAArtery occlusionInfarct volumeIschemic strokeClinical predictorsConsecutive patients