2024
Deep 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
2022
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 makingMachine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis
Bahar RC, Merkaj S, Petersen G, Tillmanns N, Subramanian H, Brim WR, Zeevi T, Staib L, Kazarian E, Lin M, Bousabarah K, Huttner AJ, Pala A, Payabvash S, Ivanidze J, Cui J, Malhotra A, Aboian MS. Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis. Frontiers In Oncology 2022, 12: 856231. PMID: 35530302, PMCID: PMC9076130, DOI: 10.3389/fonc.2022.856231.Peer-Reviewed Original ResearchMachine learning modelsLearning modelConvolutional neural networkDeep learning studiesLarge training datasetsGrade predictionSupport vector machineApplication of MLNeural networkConventional machineVector machineTraining datasetBest performing modelCommon algorithmsModel performanceEssential metricMean prediction accuracyHigh predictive accuracyPrediction accuracyPerforming modelMachinePrediction modelDiagnosis statementsAccuracy statementsLearning studies
2019
Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers
Payabvash S, Palacios EM, Owen JP, Wang MB, Tavassoli T, Gerdes M, Brandes-Aitken A, Marco EJ, Mukherjee P. Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers. NeuroImage Clinical 2019, 23: 101831. PMID: 31035231, PMCID: PMC6488562, DOI: 10.1016/j.nicl.2019.101831.Peer-Reviewed Original ResearchConceptsPosterior white matter tractsSupport vector machineAccurate classification rateNaïve BayesDifferent machineNeural networkVector machineRandom forestClassification rateRandom forest modelMachineEdge densityConnectivity metricsAlgorithmDTI/High accuracyForest modelMetricsAccuracyBrain's inabilityBayesClassifierNetworkSensory processing disordersClassification