2023
Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging
Bajaj S, Khunte M, Moily N, Payabvash S, Wintermark M, Gandhi D, Malhotra A. Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging. Journal Of The American College Of Radiology 2023, 20: 1241-1249. PMID: 37574094, DOI: 10.1016/j.jacr.2023.06.034.Peer-Reviewed Original ResearchConceptsArtificial intelligence algorithmsAI algorithmsIntelligence algorithmsValue propositionUser timeAI developersMost vendorsProduct informationDevelopersAlgorithmVendorsProduct websitesDeveloper websitesWebsitesUser testimonialsCentral databaseTechnologyDatabaseInformationAIDevicesAdoptionPropositionCostArtificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms
Berson E, Aboian M, Malhotra A, Payabvash S. Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms. Seminars In Roentgenology 2023, 58: 178-183. PMID: 37087138, PMCID: PMC10122717, DOI: 10.1053/j.ro.2023.03.002.Peer-Reviewed Original Research
2022
Dataset on acute stroke risk stratification from CT angiographic radiomics
Avery EW, Behland J, Mak A, Haider SP, Zeevi T, Sanelli PC, Filippi CG, Malhotra A, Matouk CC, Griessenauer CJ, Zand R, Hendrix P, Abedi V, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S. Dataset on acute stroke risk stratification from CT angiographic radiomics. Data In Brief 2022, 44: 108542. PMID: 36060820, PMCID: PMC9428796, DOI: 10.1016/j.dib.2022.108542.Peer-Reviewed Original ResearchMachine Learning FrameworkImage processing technologyFeature selection algorithmField of radiomicsRadiomics-based analysisMachine learningMedical imagesSelection algorithmAssistance toolRadiomic featuresRadiomics dataProcessing technologyAnalysis frameworkRelevant informationRadiomics algorithmAlgorithmCT angiography imagesRadiomicsMethodological supportExternal testingFrameworkImagesAngiography imagesMachineFeaturesMachine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities
Merkaj S, Bahar RC, Zeevi T, Lin M, Ikuta I, Bousabarah K, Petersen G, Staib L, Payabvash S, Mongan JT, Cha S, Aboian MS. Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers 2022, 14: 2623. PMID: 35681603, PMCID: PMC9179416, DOI: 10.3390/cancers14112623.Peer-Reviewed Original ResearchMachine learning toolsGrade predictionLearning toolsML applicationsClassifier algorithmML modelsClassification methodMedical imagingData sourcesPractices of radiologistsToolGlioma gradingNext stepWorkflowAlgorithmChallengesTechnological innovationImplementationPredictionModelLast decadeSpecific areasIdentifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries
Tillmanns N, Lum AE, Cassinelli G, Merkaj S, Verma T, Zeevi T, Staib L, Subramanian H, Bahar RC, Brim W, Lost J, Jekel L, Brackett A, Payabvash S, Ikuta I, Lin M, Bousabarah K, Johnson MH, Cui J, Malhotra A, Omuro A, Turowski B, Aboian MS. Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries. Neuro-Oncology Advances 2022, 4: vdac093. PMID: 36071926, PMCID: PMC9446682, DOI: 10.1093/noajnl/vdac093.Peer-Reviewed Original ResearchGlioma segmentationResearch algorithmSegmentation of gliomasHigh accuracy resultsML algorithmsApplicable machineAccuracy resultsTCIA datasetSegmentationAlgorithmMachinePatient dataSystematic literature reviewOverfittingData extractionDatasetBratDatabaseRecent advancesResearch literatureLimitationsExtractionCurrent research literatureMethod
2020
A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness
Avadiappan S, Payabvash S, Morrison MA, Jakary A, Hess CP, Lupo JM. A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness. Frontiers In Neuroscience 2020, 14: 537. PMID: 32612496, PMCID: PMC7308498, DOI: 10.3389/fnins.2020.00537.Peer-Reviewed Original ResearchAutomatic segmentationManual segmentationDice similarity coefficientEntire 3D volumeSegmentation of vesselsMagnetic resonance angiography imagesSegmentation accuracyImage processingSegmentation algorithmSynthetic datasetsF-scoreRobust segmentationDifferent noise levelsNovel algorithmSegmentationFrangi filterPrior methodsJaccard indexNoisy conditionsLow contrastMRA datasetsDatasetAutomated methodSimilarity coefficientAlgorithmMachine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings
Payabvash S, Aboian M, Tihan T, Cha S. Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings. Frontiers In Oncology 2020, 10: 71. PMID: 32117728, PMCID: PMC7018938, DOI: 10.3389/fonc.2020.00071.Peer-Reviewed Original ResearchDecision tree modelDifferent machineTree modelAccurate classification rateAveraged AUCClassification algorithmsTraining datasetRandom forestDecision treeClassification rateRandom forest modelMachineAlgorithmTerminal nodesHigh accuracyForest modelDecision modelHistogram analysisDichotomized classificationClassificationIntra-axial posterior fossa tumorsAccuracyDatasetGreater accuracyNodes
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