Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease
Sorby-Adams A, Guo J, Laso P, Kirsch J, Zabinska J, Garcia Guarniz A, Schaefer P, Payabvash S, de Havenon A, Rosen M, Sheth K, Gomez-Isla T, Iglesias J, Kimberly W. Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease. Nature Communications 2024, 15: 10488. PMID: 39622805, PMCID: PMC11612292, DOI: 10.1038/s41467-024-54972-x.Peer-Reviewed Original ResearchConceptsWhite matter hyperintensitiesMachine learning pipelineMild cognitive impairmentAlzheimer's diseaseWhite matter hyperintensities volumeLearning pipelineAssessment of patientsIncrease accessCognitive impairmentEvaluation of Alzheimer's diseaseDementiaLF-MRIPoint-of-care assessmentMagnetic resonance imagingHippocampal volumeResonance imagingImage qualityDiseaseReduce costsAnisotropic counterpartIncreasing availabilityManual segmentationDeep 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 stroke