Deep Learning Applications for Acute Stroke Management
Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, Kim JA, Sharma R, Schiff SJ, Rosen MS, Kalpathy‐Cramer J, Gonzalez J, Kimberly WT, Sheth KN. Deep Learning Applications for Acute Stroke Management. Annals Of Neurology 2022, 92: 574-587. PMID: 35689531, DOI: 10.1002/ana.26435.Peer-Reviewed Original ResearchConceptsDeep machine learningDeep learning applicationsMedical image analysisDeep neural networksPixel-wise labelingAcute stroke managementReal-world examplesDL applicationsDL approachMachine learningLearning applicationsDL modelsNeural networkStroke managementLesion segmentationMaximal utilityImage analysisElectronic medical record dataInter-rater variabilityCause of disabilityMedical record dataRelevant clinical featuresStroke detectionAdvanced neuroimaging techniquesDecision makingBedside detection of intracranial midline shift using portable magnetic resonance imaging
Sheth KN, Yuen MM, Mazurek MH, Cahn BA, Prabhat AM, Salehi S, Shah JT, By S, Welch EB, Sofka M, Sacolick LI, Kim JA, Payabvash S, Falcone GJ, Gilmore EJ, Hwang DY, Matouk C, Gordon-Kundu B, RN AW, Petersen N, Schindler J, Gobeske KT, Sansing LH, Sze G, Rosen MS, Kimberly WT, Kundu P. Bedside detection of intracranial midline shift using portable magnetic resonance imaging. Scientific Reports 2022, 12: 67. PMID: 34996970, PMCID: PMC8742125, DOI: 10.1038/s41598-021-03892-7.Peer-Reviewed Original ResearchConceptsMidline shiftNeuroscience intensive care unitCare measurementYale-New Haven HospitalValuable bedside toolIntensive care unitPoor clinical outcomeBrain-injured patientsMass effectNew Haven HospitalMagnetic resonance imagingClinical outcomesIll patientsCare unitStroke patientsFunctional outcomeBedside toolObservational studyBedside detectionImaging examsPatientsResonance imagingPortable MRIImaging suiteSignificant concordance