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 strokeRadiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke
Avery E, Abou-Karam A, Abi-Fadel S, Behland J, Mak A, Haider S, Zeevi T, Sanelli P, Filippi C, Malhotra A, Matouk C, Falcone G, Petersen N, Sansing L, Sheth K, Payabvash S. Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke. Diagnostics 2024, 14: 485. PMID: 38472957, PMCID: PMC10930945, DOI: 10.3390/diagnostics14050485.Peer-Reviewed Original ResearchCollateral statusCollateral scoreLarge vessel occlusionAcute LVO strokeRadiomics modelTest cohortCT angiography of patientsAdmission computed tomography angiographyAnterior circulation territoryAngiography of patientsLong-term outcomesReceiver operating characteristic areaRadiomics-based predictionCollateral arterial circulationOperating characteristics areaAdmission CTACirculation territoryCT angiographyClinical outcomesRadiomic featuresTreatment triageOcclusion strokeVessel occlusionPatientsArterial circulationUncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
Tran A, Zeevi T, Haider S, Abou Karam G, Berson E, Tharmaseelan H, Qureshi A, Sanelli P, Werring D, Malhotra A, Petersen N, de Havenon A, Falcone G, Sheth K, Payabvash S. Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. Npj Digital Medicine 2024, 7: 26. PMID: 38321131, PMCID: PMC10847454, DOI: 10.1038/s41746-024-01007-w.Peer-Reviewed Original ResearchDeep learning modelsHematoma expansionIntracerebral hemorrhageICH expansionComputed tomographyNon-contrast head CTNon-contrast head computed tomographyHigh risk of HEHead computed tomographyHigh-confidence predictionsRisk of HENon-contrast headReceiver operating characteristic areaModifiable risk factorsMonte Carlo dropoutOperating characteristics areaPotential treatment targetHead CTVisual markersIdentified patientsAutomated deep learning modelDataset of patientsRisk factorsHigh riskPatients