Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)
Dierksen F, Sommer J, Tran A, Lin H, Haider S, Maier I, Aneja S, Sanelli P, Malhotra A, Qureshi A, Claassen J, Park S, Murthy S, Falcone G, Sheth K, Payabvash S. Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT). Diagnostics 2024, 14: 2827. PMID: 39767188, PMCID: PMC11674633, DOI: 10.3390/diagnostics14242827.Peer-Reviewed Original ResearchIntegrated discrimination indexNet reclassification indexPerihematomal edemaHead computed tomographyIntracerebral hemorrhageComputed tomographyClinical variablesClinical predictors of poor outcomeOutcome predictionAcute supratentorial intracerebral hemorrhageAdmission head computed tomographyRadiomic featuresNon-contrast head computed tomographyPredictors of poor outcomeModified Rankin Scale scoreIntracerebral hemorrhage scoreSupratentorial intracerebral hemorrhageIntracerebral hemorrhage patientsClinical risk factorsRankin Scale scoreReceiver operating characteristic areaOperating characteristics areaSecondary brain injuryHematoma evacuationPatient selectionA Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence
Tran A, Desser D, Zeevi T, Abou Karam G, Dierksen F, Dell'Orco A, Kniep H, Hanning U, Fiehler J, Zietz J, Sanelli P, Malhotra A, Duncan J, Aneja S, Falcone G, Qureshi A, Sheth K, Nawabi J, Payabvash S. A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence. Bioengineering 2024, 11: 1274. PMID: 39768092, PMCID: PMC11672977, DOI: 10.3390/bioengineering11121274.Peer-Reviewed Original ResearchNon-contrast head computed tomographyPerihematomal edemaHead computed tomographyIntracerebral hemorrhageComputed tomographyVolume similarityUniversity Medical Center Hamburg-EppendorfSecondary brain injuryYale cohortInfratentorial locationMulticentre trialCT scanTreatment planningNon-contrastHamburg-EppendorfImaging markersHemorrhagic strokeHemorrhageEdemaCohortBrain injuryDice coefficientUncertainty-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
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