2025
Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Shankar S, Coppi A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD. Journal Of The American College Of Cardiology 2025, 85: 1302-1313. PMID: 40139886, DOI: 10.1016/j.jacc.2025.01.030.Peer-Reviewed Original ResearchConceptsStructural heart diseaseYale-New Haven HospitalTransthoracic echocardiogramRisk stratificationHeart failureLeft-sided valvular diseaseSevere left ventricular hypertrophyLeft ventricular ejection fractionReceiver-operating characteristic curveVentricular ejection fractionLeft ventricular hypertrophyHeart disease screeningELSA-BrasilEnsemble deep learning algorithmRisk of deathConvolutional neural network modelEjection fractionEnsemble deep learning approachVentricular hypertrophyDeep learning algorithmsNew Haven HospitalDeep learning approachValvular diseaseNeural network modelClinical cohort
2024
Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis
Sharifi G, Hajibeygi R, Zamani S, Easa A, Bahrami A, Eshraghi R, Moafi M, Ebrahimi M, Fathi M, Mirjafari A, Chan J, Dixe de Oliveira Santo I, Anar M, Rezaei O, Tu L. Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis. Emergency Radiology 2024, 32: 97-111. PMID: 39680295, DOI: 10.1007/s10140-024-02300-7.Peer-Reviewed Original ResearchConceptsConvolutional neural networkArea under the receiver operating characteristic curveConvolutional neural network modelCT scanSkull fractureComputed tomographyDeep learningProspective clinical trialMeta-analysisReceiver operating characteristic curvePublication biasSkull fracture detectionSystematic reviewNeural network algorithmDetecting skull fracturesImprove diagnosis accuracyDiagnostic hurdlesShortage of radiologistsAutomated diagnostic toolTransfer learningDiagnostic performanceDiagnostic accuracyClinical trialsModel architectureNeural networkDeep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability
Zhou X, Kedia S, Meng R, Gerstein M. Deep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability. PLOS ONE 2024, 19: e0312848. PMID: 39630834, PMCID: PMC11616848, DOI: 10.1371/journal.pone.0312848.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkAlzheimer's diseaseConvolutional neural network modelMultimodal medical datasetsDeep learning methodsPotential of deep learningGenetic risk factorsMedical datasetsAlzheimer's Disease Neuroimaging InitiativeAD predictionDeep learningDeep learning analysisLearning methodsMedical imagesPredicting Alzheimer's diseaseDetection of Alzheimer's diseaseModel interpretationEarly detection of Alzheimer's diseaseAccuracy levelGenetic factorsDatasetEarly detection of ADNetworkDetection of AD
2023
Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence
Tveit J, Aurlien H, Plis S, Calhoun V, Tatum W, Schomer D, Arntsen V, Cox F, Fahoum F, Gallentine W, Gardella E, Hahn C, Husain A, Kessler S, Kural M, Nascimento F, Tankisi H, Ulvin L, Wennberg R, Beniczky S. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurology 2023, 80: 805-812. PMID: 37338864, PMCID: PMC10282956, DOI: 10.1001/jamaneurol.2023.1645.Peer-Reviewed Original ResearchConceptsPublishing AI modelsAI modelsArtificial intelligenceTesting data setsHuman expertsAutomated interpretationConvolutional neural network modelHuman expert level performanceElectroencephalogram data setsData setsRoutine electroencephalogramNeural network modelExpert-level performanceMulticenter diagnostic accuracy studyReference standardAbnormal EEG recordingsVideo-EEG recordingsDetection of epileptiform abnormalitiesRecords of patientsReceiver operating characteristic curveSingle-center dataArea under the receiver operating characteristic curveDevelopment dataDiagnostic accuracy studiesNetwork model
2021
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI
Liu X, Xing F, Gaggin H, Wang W, Kuo C, Fakhri G, Woo J. Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2021, 00: 3535-3538. PMID: 34892002, DOI: 10.1109/embc46164.2021.9629770.Peer-Reviewed Original ResearchConceptsConvolutional neural networkSegmentation of cardiac structuresSaab transformSubspace approximationAccurate segmentation of cardiac structuresDimension reductionConvolutional neural network modelConcatenation of featuresUnsupervised dimension reductionConditional random fieldPixel-wise classificationSupervised dimension reductionU-Net modelMachine learning modelsSubspace learningChannel-wiseSegmentation databaseSegmentation frameworkNeural networkU-NetEfficient segmentationAccurate segmentationLearning modelsCardiac MR imagesRandom field
2019
Temporal indexing of medical entity in Chinese clinical notes
Liu Z, Wang X, Chen Q, Tang B, Xu H. Temporal indexing of medical entity in Chinese clinical notes. BMC Medical Informatics And Decision Making 2019, 19: 17. PMID: 30700331, PMCID: PMC6354334, DOI: 10.1186/s12911-019-0735-x.Peer-Reviewed Original ResearchConceptsSupport vector machineConvolutional neural networkTemporal indexingNeural network modelIndexing taskRelation classificationMedical entitiesRecurrent convolutional neural network modelMachine learning-based systemsConvolutional neural network modelDeep neural network modelNetwork methodNetwork modelLearning-based systemTemporal relation classificationRecurrent neural network methodChinese clinical notesTemporal relationsClinical notesNeural network methodI2b2 NLP challengeContext informationTime indexingSemantic informationBaseline methods
2018
Common Object Representations for Visual Production and Recognition
Fan JE, Yamins DLK, Turk‐Browne N. Common Object Representations for Visual Production and Recognition. Cognitive Science 2018, 42: 2670-2698. PMID: 30125986, PMCID: PMC6497164, DOI: 10.1111/cogs.12676.Peer-Reviewed Original ResearchConceptsVisual object recognitionStudy of visionDeep convolutional neural network modelNatural imagesConvolutional neural network modelAbstract feature representationRecognizable drawingsHuman learningObject representationsVisual productionNeural network modelObject recognitionConceptual knowledgeVisual formVisual conceptsVisual cortexRecognition dataDeep networkFeature representationEnhanced recognitionAbstract featuresComprehensionHigher layersNetwork modelOnline platforms
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