Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.
Chen Y, Rivier C, Mora S, Torres Lopez V, Payabvash S, Sheth K, Harloff A, Falcone G, Rosand J, Mayerhofer E, Anderson C. Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan. European Stroke Journal 2024, 23969873241260154. PMID: 38880882, DOI: 10.1177/23969873241260154.Peer-Reviewed Original ResearchIntracerebral hemorrhage scoreNon-contrast CT scanIntracerebral hemorrhageCT scanFUNC scoreIntracerebral hemorrhage patientsNon-contrast CTFunctional impairmentSevere disabilityDependent living statusLong-term functional impairmentC-indexPrognostic toolFunctional outcomesTreatment decisionsAcute settingClinical implementationRehabilitation strategiesDependent livingPatientsPredicting functional impairmentLong-term care needsPlanning of patient careDeep learning modelsHemorrhageUncertainty-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