2022
Reticular Pseudodrusen: The Third Macular Risk Feature for Progression to Late Age-Related Macular Degeneration Age-Related Eye Disease Study 2 Report 30
Agrón E, Domalpally A, Cukras C, Clemons T, Chen Q, Lu Z, Chew E, Keenan T, Groups A. Reticular Pseudodrusen: The Third Macular Risk Feature for Progression to Late Age-Related Macular Degeneration Age-Related Eye Disease Study 2 Report 30. Ophthalmology 2022, 129: 1107-1119. PMID: 35660417, PMCID: PMC9509418, DOI: 10.1016/j.ophtha.2022.05.021.Peer-Reviewed Original ResearchConceptsLate age-related macular degenerationAge-related macular degenerationAge-related eye disease studyNeovascular age-related macular degenerationColor fundus photographsHazard ratioReticular pseudodrusenGeographic atrophyHigh riskRisk factorsSeverity ScalePresence of RPDProportional hazards regression analysisMacular Degeneration AgeClinical trial cohortIndependent risk factorEye Disease StudyHazards regression analysisImportant risk factorFundus autofluorescence imagesAMD severity scaleTrial cohortRisk calculatorClinical trialsFundus photographsMulti-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 ResearchMeSH KeywordsDeep LearningDisease ProgressionFundus OculiHumansMacular DegenerationPrognosisSurvival AnalysisConceptsAge-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 paceFeaturesNetworkAREDSPatients
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