2024
An Updated Simplified Severity Scale for Age-Related Macular Degeneration Incorporating Reticular Pseudodrusen Age-Related Eye Disease Study Report Number 42
Agrón E, Domalpally A, Chen Q, Lu Z, Chew E, Keenan T, Groups A. An Updated Simplified Severity Scale for Age-Related Macular Degeneration Incorporating Reticular Pseudodrusen Age-Related Eye Disease Study Report Number 42. Ophthalmology 2024, 131: 1164-1174. PMID: 38657840, PMCID: PMC11416341, DOI: 10.1016/j.ophtha.2024.04.011.Peer-Reviewed Original ResearchAge-Related Eye Disease StudyProgression to late AMDReticular pseudodrusenLate AMDFive-year ratesProgression rateAge-related macular degenerationSeverity ScaleEye Disease StudyClinical trial cohortIncrease prognostic accuracyPost hoc analysisMacular degenerationAREDS2Prognostic accuracyTrial cohortRisk featuresHoc analysisRisk categorizationPseudodrusenAge-relatedBaselineDisease StudyRiskExternal validationDetection of reticular pseudodrusen on optical coherence tomography images
Elsawy A, Keenan T, Agron E, Chen Q, Chew E, Lu Z. Detection of reticular pseudodrusen on optical coherence tomography images. Progress In Biomedical Optics And Imaging 2024, 12926: 1292632-1292632-5. DOI: 10.1117/12.3007014.Peer-Reviewed Original ResearchAge-related macular degenerationSD-OCT scansAge-Related Eye Disease Study 2Detect reticular pseudodrusenReticular pseudodrusenSD-OCTFundus autofluorescenceVolumetric spectral-domain optical coherence tomographySpectral-domain optical coherence tomographySubretinal drusenoid depositsOptical coherence tomography imagesPredictors of progressionOptical coherence tomographyReceiver characteristic operating curvesDrusenoid depositsMacular degenerationOCT studiesCoherence tomographyDisease featuresTomography imagesOperating curvePseudodrusenAge-relatedClassification networkMulti-tasking
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 paceFeaturesNetworkAREDSPatients
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 ResearchMultimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration
Chen Q, Keenan T, Allot A, Peng Y, Agrón E, Domalpally A, Klaver C, Luttikhuizen D, Colyer M, Cukras C, Wiley H, Magone M, Cousineau-Krieger C, Wong W, Zhu Y, Chew E, Lu Z, Group F. Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration. Journal Of The American Medical Informatics Association 2021, 28: 1135-1148. PMID: 33792724, PMCID: PMC8200273, DOI: 10.1093/jamia/ocaa302.Peer-Reviewed Original ResearchConceptsColor fundus photographyAge-related macular degenerationFundus autofluorescenceReticular pseudodrusenMacular degenerationStandard color fundus photographyReceiver-operating characteristic curveAdvanced imaging modalitiesExternal validationRetinal specialistsAMD featuresFundus photographyGeographic atrophyPigmentary abnormalitiesAMD diagnosisImaging modalitiesCharacteristic curvePseudodrusenDegeneration
2020
Predicting risk of late age-related macular degeneration using deep learning
Peng Y, Keenan T, Chen Q, Agrón E, Allot A, Wong W, Chew E, Lu Z. Predicting risk of late age-related macular degeneration using deep learning. Npj Digital Medicine 2020, 3: 111. PMID: 32904246, PMCID: PMC7453007, DOI: 10.1038/s41746-020-00317-z.Peer-Reviewed Original ResearchLate age-related macular degenerationAge-related macular degenerationHigher prognostic accuracyClinical standardsMacular degenerationPrognostic accuracyIndependent cohortLargest longitudinal clinical trialsProbability of progressionSight-threatening stagesColor fundus photographsLongitudinal clinical trialsAMD patientsRetinal specialistsClinical trialsFundus photographsSpecialty clinicHigh riskClinical actionsSurvival analysisMedical interventionsIndividual riskAREDS2AREDSExternal validation
2019
A multi-task deep learning model for the classification of Age-related Macular Degeneration.
Chen Q, Peng Y, Keenan T, Dharssi S, Agro N E, Wong W, Chew E, Lu Z. A multi-task deep learning model for the classification of Age-related Macular Degeneration. AMIA Joint Summits On Translational Science Proceedings 2019, 2019: 505-514. PMID: 31259005, PMCID: PMC6568069.Peer-Reviewed Original ResearchDeep learning modelsLearning modelMulti-task deep learning modelNovel deep learning modelMulti-task learning techniquesColor fundus imagesImage datasetsLearning techniquesAutomated classificationManual classificationArt modelsManual gradingFundus imagesGrading processClassificationImagesAge-related macular degenerationCurrent stateEye Disease Study GroupAMD severity scaleOverfittingDatasetMacular degenerationModelAccuracy
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