2024
Data augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model
Yang Y, Ma S, Cao S, Jia S, Bi Y, Calhoun V. Data augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model. Proceedings Of SPIE--the International Society For Optical Engineering 2024, 13252: 1325214-1325214-7. DOI: 10.1117/12.3044654.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional network connectivity matricesIndependent component analysisVision Transformer (ViTAdvanced artificial intelligence techniquesTraditional U-NetArtificial intelligence techniquesFunctional magnetic resonance imaging dataGroup independent component analysisNetwork connectivity matrixDenoising functionData augmentationImage generationIntelligence techniquesU-NetSmall datasetsDiagnosed schizophreniaSchizophrenia diagnosisGeneration taskNeuroimaging dataSchizophreniaComputational burdenConnectivity matrixMagnetic resonance imagingRelevant informationNeuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade
Du Y, Niu J, Xing Y, Li B, Calhoun V. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophrenia Bulletin 2024, 51: 325-342. PMID: 38982882, PMCID: PMC11908864, DOI: 10.1093/schbul/sbae110.Peer-Reviewed Original ResearchArtificial intelligenceSemi-supervised learning methodArtificial intelligence techniquesAccurate diagnosis of SZMultimodal fusionAccurate diagnosis of schizophreniaIntelligence techniquesAI methodsLearning methodsDiagnosis of SZMental disordersSelection methodUnsupervised clusteringMagnetic resonance imagingBiomarker extractionDiagnosis of schizophrenia
2023
“Big Data” Analyses Underlie Clinical Discoveries at the Aortic Institute
Zafar M, Ziganshin B, Li Y, Ostberg N, Rizzo J, Tranquilli M, Mukherjee S, Elefteriades J. “Big Data” Analyses Underlie Clinical Discoveries at the Aortic Institute. The Yale Journal Of Biology And Medicine 2023, 96: 427-440. PMID: 37780996, PMCID: PMC10524815, DOI: 10.59249/lndz2964.Peer-Reviewed Original ResearchConceptsBig dataArtificial intelligenceThoracic aortic aneurysmArtificial intelligence techniquesData setsAortic aneurysmLarge clinical data setsIntelligence techniquesAdvanced machineYale-New Haven HospitalClinical data setsAortic aneurysm patientsAortic root dilatationBicuspid aortic valveProspective clinical databaseNew Haven HospitalLarge genetic data setsIntelligenceSequencing filesThoracoabdominal aortaAortic dilatationMale patientsRisk prognosticationRoot dilatationSurgical treatment
2022
Creating High Fidelity Synthetic Pelvis Radiographs Using Generative Adversarial Networks: Unlocking the Potential of Deep Learning Models Without Patient Privacy Concerns
Khosravi B, Rouzrokh P, Mickley J, Faghani S, Larson A, Garner H, Howe B, Erickson B, Taunton M, Wyles C. Creating High Fidelity Synthetic Pelvis Radiographs Using Generative Adversarial Networks: Unlocking the Potential of Deep Learning Models Without Patient Privacy Concerns. The Journal Of Arthroplasty 2022, 38: 2037-2043.e1. PMID: 36535448, DOI: 10.1016/j.arth.2022.12.013.Peer-Reviewed Original ResearchConceptsAdversarial networkSynthetic imagesDL modelsImage fidelityAssessment of image fidelityPerformance of DL modelsGenerative adversarial networkDeep learning modelsCross-institutional sharingArtificial intelligence techniquesPatient privacy concernsPotential of deep learning modelsReal imagesPrivacy concernsDL techniquesIntelligence techniquesRandom imagesLearning modelsPatient privacyPaired imagesReal radiographsData safetyPelvis imagesNetworkHigh-fidelity
1988
Artificial intelligence research in anesthesia and intensive care
Rennels G, Miller P. Artificial intelligence research in anesthesia and intensive care. Journal Of Clinical Monitoring 1988, 4: 274-289. PMID: 3057121, DOI: 10.1007/bf01617327.Peer-Reviewed Original ResearchConceptsArtificial intelligence techniquesIntelligence techniquesArtificial intelligenceArtificial intelligence researchComputer science researchMedical artificial intelligenceChallenging domainComputer systemsIntelligence researchIntelligenceDifferent projectsResearch directionsCentral research themeSpecific research projectsCurrent stateScience researchResearch projectResearch themesTechniqueProjectManagementApplications
1984
Medical plan-analysis by computer: Critiquing the pharmacologic management of essential hypertension
Miller P, Black H. Medical plan-analysis by computer: Critiquing the pharmacologic management of essential hypertension. Journal Of Biomedical Informatics 1984, 17: 38-54. PMID: 6697700, DOI: 10.1016/0010-4809(84)90005-3.Peer-Reviewed Original ResearchConceptsArtificial intelligence techniquesPharmacologic managementEssential hypertensionInteractive paperIntelligence techniquesPatient careComputer systemsNew treatment modalitiesPhysician's management planHypertensive patientsTreatment modalitiesPhysicians' approachNew drugsCareHypertensionMedical practiceManagement errorsComputerPatientsMedical plan-analysis by computer
Miller P. Medical plan-analysis by computer. Computer Methods And Programs In Biomedicine 1984, 18: 15-19. PMID: 6547652, DOI: 10.1016/0010-468x(84)90019-9.Peer-Reviewed Original Research
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply