2024
Uncertainty-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 riskPatientsTime-Dependent Changes in Hematoma Expansion Rate after Supratentorial Intracerebral Hemorrhage and Its Relationship with Neurological Deterioration and Functional Outcome
Karam G, Chen M, Zeevi D, Harms B, Torres-Lopez V, Rivier C, Malhotra A, de Havenon A, Falcone G, Sheth K, Payabvash S. Time-Dependent Changes in Hematoma Expansion Rate after Supratentorial Intracerebral Hemorrhage and Its Relationship with Neurological Deterioration and Functional Outcome. Diagnostics 2024, 14: 308. PMID: 38337824, PMCID: PMC10855868, DOI: 10.3390/diagnostics14030308.Peer-Reviewed Original ResearchPredictors of NDSupratentorial intracerebral hemorrhageHematoma expansionIntracerebral hemorrhageNeurological deteriorationPoor outcomeNIH Stroke ScalePost-ICHFunctional outcomesMild symptomsHematoma expansion ratesIntracerebral hemorrhage onsetAssociation of HEModifiable risk factorsGlasgow Coma ScaleBaseline CTHematoma growthConsecutive patientsHead CTSCAN-3Follow-upRate of HEComa ScaleRisk factorsHematoma
2022
Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports
Torres-Lopez VM, Rovenolt GE, Olcese AJ, Garcia GE, Chacko SM, Robinson A, Gaiser E, Acosta J, Herman AL, Kuohn LR, Leary M, Soto AL, Zhang Q, Fatima S, Falcone GJ, Payabvash MS, Sharma R, Struck AF, Sheth KN, Westover MB, Kim JA. Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports. JAMA Network Open 2022, 5: e2227109. PMID: 35972739, PMCID: PMC9382443, DOI: 10.1001/jamanetworkopen.2022.27109.Peer-Reviewed Original ResearchConceptsNatural language processingF-scoreTest data setsLanguage processingIndependent test data setsData setsBidirectional Encoder RepresentationsAcute brain injuryLarge data setsHead CTBrain injuryNLP toolsF1 scoreNER modelTransformer architectureClinical textEncoder RepresentationsNLP algorithmNLP modelsCT reportsCustom dictionaryTraining setCross-validation performancePerformance metricsAvailable new tools