2024
In vivo neuropil density from anatomical MRI and machine learning
Akif A, Staib L, Herman P, Rothman D, Yu Y, Hyder F. In vivo neuropil density from anatomical MRI and machine learning. Cerebral Cortex 2024, 34: bhae200. PMID: 38771239, PMCID: PMC11107380, DOI: 10.1093/cercor/bhae200.Peer-Reviewed Original ResearchConceptsMagnetic resonance imagingSynaptic densityNeuropil densityCellular densityArtificial neural networkNeural networkPositron emission tomographyAnatomical magnetic resonance imagingHealthy subjectsSynaptic activityMRI scansMachine learning algorithmsBrain's energy budgetEmission tomographyIn vivo MRI scansResonance imagingTissue cellularityLearning algorithmsDiffusion magnetic resonance imagingMachine learningMicroscopic interpretationInterpretation of functional neuroimaging dataIndividual predictionsSubjects
2022
Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
Petersen G, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. American Journal Of Neuroradiology 2022, 43: 526-533. PMID: 35361577, PMCID: PMC8993193, DOI: 10.3174/ajnr.a7473.Peer-Reviewed Original ResearchConceptsMachine learning-based methodsLearning-based methodsBalanced data setData setsVector machine modelMachine learningClassification algorithmsMachine modelMachineAlgorithmData basesPrediction modelPromising resultsPrimary CNS lymphomaPrediction model study RiskRisk of biasRadiomic featuresClassifierSetCNS lymphomaWebLearningFeaturesQualitySystematic review
2020
PROOF-OF-CONCEPT USE OF MACHINE LEARNING TO PREDICT TUMOR RECURRENCE OF EARLY-STAGE HEPATOCELLULAR CARCINOMA BEFORE THERAPY USING BASELINE MAGNETIC RESONANCE IMAGING
Batra R, Kuecuekkaya A, Zeevi T, Raju R, Chai N, Haider S, Elbanan M, Petukhova A, Lin ,, Onofrey J, Nowak M, Cooper K, Thomas E, Gebauer B, Staib L, Chapiro J. PROOF-OF-CONCEPT USE OF MACHINE LEARNING TO PREDICT TUMOR RECURRENCE OF EARLY-STAGE HEPATOCELLULAR CARCINOMA BEFORE THERAPY USING BASELINE MAGNETIC RESONANCE IMAGING. Transplantation 2020, 104: s43-s44. DOI: 10.1097/01.tp.0000698472.65040.1e.Peer-Reviewed Original ResearchMachine learning