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 imagesMachineFeaturesClinical Implementation of Novel PACS-based Deep Learning Glioma Segmentation Algorithm
Merkaj S, Bousabarah K, MingDe L, Pala A, Petersen G, Jekel L, Bahar R, Tillmanns N, Malhotra A, Westerhoff M, Aboian M. Clinical Implementation of Novel PACS-based Deep Learning Glioma Segmentation Algorithm. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2022 DOI: 10.58530/2022/2744.Peer-Reviewed Original ResearchReal-time buildingAuto-segmentation algorithmTumor segmentationSegmentation algorithmBRATS datasetData generationNew segmentationData productionPrediction algorithmClinical workflowAlgorithmDatasetLaborious processHospital datasetPACSSegmentationNovel approachWorkflowImagesImplementationExpertsGenerationRetrainingIdentifying 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
2021
NIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMAS
Petersen G, Shatalov J, Brim W, Subramanian H, cui J, Johnson M, Malhotra A, Aboian M, Brackett A. NIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMAS. Neuro-Oncology 2021, 23: vi144-vi145. PMCID: PMC8598874, DOI: 10.1093/neuonc/noab196.565.Peer-Reviewed Original ResearchMachine learningDL algorithmsApplication of MLDeep learning algorithmsConvolutional neural networkMachine learning modelsSupport vector machineRisk of overfittingArtificial intelligenceLearning algorithmML algorithmsNeural networkVector machineLearning modelLarge datasetsNovel DLInternal datasetML methodsAlgorithmAverage AUCSearch strategyDatasetPromising resultsLearningRelated termsNIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW
Tillmanns N, Lum A, Brim W, Subramanian H, Lin M, Bousabarah K, Malhotra A, cui J, Brackett A, Payabvash S, Ikuta I, Johnson M, Turowski B, Aboian M. NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW. Neuro-Oncology 2021, 23: vi145-vi145. PMCID: PMC8598815, DOI: 10.1093/neuonc/noab196.568.Peer-Reviewed Original ResearchConvolutional neural networkSegmentation of gliomasSupport vector machineGlioma segmentationDeep learningMachine learningLikelihood of overfittingMachine Learning AlgorithmsArtificial intelligenceLearning algorithmDice scoreML algorithmsTumor segmentationNeural networkVector machineCommon algorithmsSegmentationSame datasetML methodsTCIA datasetAlgorithmData acquisitionAccuracy reportingHigh accuracyLearning