2023
A deep network DeepOpacityNet for detection of cataracts from color fundus photographs
Elsawy A, Keenan T, Chen Q, Thavikulwat A, Bhandari S, Quek T, Goh J, Tham Y, Cheng C, Chew E, Lu Z. A deep network DeepOpacityNet for detection of cataracts from color fundus photographs. Communications Medicine 2023, 3: 184. PMID: 38104223, PMCID: PMC10725427, DOI: 10.1038/s43856-023-00410-w.Peer-Reviewed Original ResearchColor fundus photographyAnterior segment photographsSlit-lamp examinationEye Disease StudyPosterior subcapsular cataractColor fundus photographsAREDS2 participantsCataract presenceSingapore EpidemiologyDetection of cataractOphthalmology clinicFundus photographyFundus photographsSubcapsular cataractCenter gradingCataractOphthalmologistsDisease StudyBlood vesselsNuclear cataractPerson evaluationAREDS2ClinicEpidemiologyDiagnosis
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 imagesPatientsCohortDegenerationBaselineReticular 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 photographs
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 Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs
Keenan T, Dharssi S, Peng Y, Chen Q, Agrón E, Wong W, Lu Z, Chew E. A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs. Ophthalmology 2019, 126: 1533-1540. PMID: 31358385, PMCID: PMC6810830, DOI: 10.1016/j.ophtha.2019.06.005.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