2022
Machine 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