2022
Detecting visually significant cataract using retinal photograph-based deep learning
Tham Y, Goh J, Anees A, Lei X, Rim T, Chee M, Wang Y, Jonas J, Thakur S, Teo Z, Cheung N, Hamzah H, Tan G, Husain R, Sabanayagam C, Wang J, Chen Q, Lu Z, Keenan T, Chew E, Tan A, Mitchell P, Goh R, Xu X, Liu Y, Wong T, Cheng C. Detecting visually significant cataract using retinal photograph-based deep learning. Nature Aging 2022, 2: 264-271. PMID: 37118370, PMCID: PMC10154193, DOI: 10.1038/s43587-022-00171-6.Peer-Reviewed Original Research
2021
Multimodal, 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
Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2
Keenan T, Chen Q, Peng Y, Domalpally A, Agrón E, Hwang C, Thavikulwat A, Lee D, Li D, Wong W, Lu Z, Chew E. Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2. Ophthalmology 2020, 127: 1674-1687. PMID: 32447042, PMCID: PMC11079794, DOI: 10.1016/j.ophtha.2020.05.036.Peer-Reviewed Original Research
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