2024
Outpatient reception via collaboration between nurses and a large language model: a randomized controlled trial
Wan P, Huang Z, Tang W, Nie Y, Pei D, Deng S, Chen J, Zhou Y, Duan H, Chen Q, Long E. Outpatient reception via collaboration between nurses and a large language model: a randomized controlled trial. Nature Medicine 2024, 30: 2878-2885. PMID: 39009780, DOI: 10.1038/s41591-024-03148-7.Peer-Reviewed Original ResearchRandomized controlled trialsNurse-led sessionsPrimary care concernsSingle-center randomized controlled trialCollaborative modelHealthcare experiencesCare concernsPatient queriesMedical careImprove communicationReducing negative emotionsNursesHospital workflowSecondary outcomesMedical CenterLanguage modelSatisfaction feedbackReal-world deploymentProportion of queriesNegative emotionsAudio corpusHuman effortCommunication systemsPatientsCareOphthalmic care may not align with patient need: An analysis on state-wide patient needs and provider density between 2008 and 2022
Gilson A, Chen Q, Adelman R. Ophthalmic care may not align with patient need: An analysis on state-wide patient needs and provider density between 2008 and 2022. International Journal Of Medical Informatics 2024, 185: 105411. PMID: 38492409, PMCID: PMC11047060, DOI: 10.1016/j.ijmedinf.2024.105411.Peer-Reviewed Original ResearchProvider densityPatient needsDensity of ophthalmologistsOphthalmological careHealthcare availabilityResources patientsPractice locationOphthalmologic termsPatient interestOphthalmic carePatient informationImplementation strategiesPatient's desireCareRetinal specialistsEducational backgroundOphthalmologistsPatientsOphthalmologyTrends dataNeedsHealthcareGoogle Trends dataDemographic elementsProviders
2022
Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning
Lee J, Wanyan T, Chen Q, Keenan T, Glicksberg B, Chew E, Lu Z, Wang F, Peng Y. Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning. Lecture Notes In Computer Science 2022, 13583: 11-20. PMID: 36656604, PMCID: PMC9842432, DOI: 10.1007/978-3-031-21014-3_2.Peer-Reviewed Original ResearchLate age-related macular degenerationAge-related macular degenerationColor fundus photographsEye Disease StudyRisk prediction modelMacular degeneration progressionTriaging patientsFundus photographsPatient riskAMD cohortMacular degenerationPatient historyDegeneration progressionDisease StudyProgressionRiskSubsequent time intervalsPersonalized medicineAgeFundus imagesPatientsCohortDegenerationBaselineMulti-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS.
Ghahramani G, Brendel M, Lin M, Chen Q, Keenan T, Chen K, Chew E, Lu Z, Peng Y, Wang F. Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS. AMIA Annual Symposium Proceedings 2022, 2021: 506-515. PMID: 35308963.Peer-Reviewed Original ResearchConceptsAge-related macular degenerationImage featuresMulti-task learning frameworkConvolutional neural networkVision lossLate age-related macular degenerationEye Disease StudyLearning frameworkNeural networkFundus photographsPatient riskMacular degenerationStandard featuresSevere formComplex featuresSurvival analysisCurrent visitLongitudinal dataDisease StudyHistorical dataRapid paceFeaturesNetworkAREDSPatientsPredicting myocardial infarction through retinal scans and minimal personal information
Diaz-Pinto A, Ravikumar N, Attar R, Suinesiaputra A, Zhao Y, Levelt E, Dall’Armellina E, Lorenzi M, Chen Q, Keenan T, Agrón E, Chew E, Lu Z, Gale C, Gale R, Plein S, Frangi A. Predicting myocardial infarction through retinal scans and minimal personal information. Nature Machine Intelligence 2022, 4: 55-61. DOI: 10.1038/s42256-021-00427-7.Peer-Reviewed Original ResearchVentricular end-diastolic volumeLeft ventricular massEnd-diastolic volumeMyocardial infarctionVentricular massPrimary eye diseaseFuture myocardial infarctionIncident myocardial infarctionCoronary artery diseaseBlood vessel densityRetinal imagesArtery diseaseEye clinicCardiac functionDiabetic retinopathySystemic conditionsEye diseaseHigh riskVessel densityInfarctionRetinal imagingDemographic dataOphthalmologic practicePatientsDisease
2021
Artificial Intelligence in Age-Related Macular Degeneration (AMD)
Peng Y, Chen Q, Keenan T, Chew E, Lu Z. Artificial Intelligence in Age-Related Macular Degeneration (AMD). 2021, 101-112. DOI: 10.1007/978-3-030-78601-4_8.Peer-Reviewed Original Research
2018
DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs
Peng Y, Dharssi S, Chen Q, Keenan T, Agrón E, Wong W, Chew E, Lu Z. DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs. Ophthalmology 2018, 126: 565-575. PMID: 30471319, PMCID: PMC6435402, DOI: 10.1016/j.ophtha.2018.11.015.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overArea Under CurveDeep LearningDiagnosis, Computer-AssistedDiagnostic Techniques, OphthalmologicalDisease ProgressionFemaleGeographic AtrophyHumansMaleMiddle AgedModels, TheoreticalPhotographyProspective StudiesReproducibility of ResultsRetinal DrusenRisk FactorsSensitivity and SpecificitySeverity of Illness IndexConceptsLate age-related macular degenerationAge-related macular degenerationColor fundus photographsSeverity ScaleRetinal specialistsSeverity scoreDeep learning modelsLarge drusenFundus photographsPigmentary abnormalitiesAge-related macular degeneration (AMD) severityPatient-based scoring systemsAMD risk factorsRisk of progressionLearning modelEye Disease StudyDeep learning systemGold-standard labelsRisk factorsMacular degenerationIndividual patientsGrading processPatient-based classificationPatientsScoring system