2022
Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging
Kong H, Kim J, Moon H, Park H, Kim J, Lim R, Woo J, Fakhri G, Kim D, Kim S. Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging. Scientific Reports 2022, 12: 18118. PMID: 36302815, PMCID: PMC9613909, DOI: 10.1038/s41598-022-22222-z.Peer-Reviewed Original ResearchConceptsSynthetic data augmentationData augmentationLack of training dataConventional data augmentationDeep learning methodsTraining dataLearning methodsPipeline approachAlgorithm trainingGraphical dataAutomationWaters' view radiographsModel performanceAutomated pipelinePerformancePerformance parametersAlgorithmDatasetAugmentationDataMethodPipelineRulesIndustrial workers
2015
Review of Ingested and Aspirated Foreign Bodies in Children and Their Clinical Significance for Radiologists
Pugmire B, Lim R, Avery L. Review of Ingested and Aspirated Foreign Bodies in Children and Their Clinical Significance for Radiologists. Radio Graphics 2015, 35: 1528-1538. PMID: 26295734, DOI: 10.1148/rg.2015140287.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsBronchoscopyCausticsChildChild, PreschoolEatingEmergenciesEndoscopy, Digestive SystemEsophagusForeign BodiesGastrointestinal TractGlassHumansInfantIntestinal ObstructionIntestinal PerforationMagnetsMetalsNumismaticsRadiographyRespiratory AspirationRespiratory SystemUnited StatesConceptsAspirated foreign bodyForeign bodyClinical managementClinical significanceIngested foreign bodiesRisk of esophageal injuryImaging appearanceAggressive clinical managementForeign body aspirationExamination of childrenEsophageal injuryBowel perforationFistula formationPrompt recognitionImaging examinationsPediatric populationClinical symptomsComputed tomographyAppropriate treatmentHigh riskPrompt identificationBowel wallRadiologistsGastrointestinal tractClinical implications