2024
Ophthalmic 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
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
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 ResearchDeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity
Keenan T, Chen Q, Agrón E, Tham Y, Goh J, Lei X, Ng Y, Liu Y, Xu X, Cheng C, Bikbov M, Jonas J, Bhandari S, Broadhead G, Colyer M, Corsini J, Cousineau-Krieger C, Gensheimer W, Grasic D, Lamba T, Magone M, Maiberger M, Oshinsky A, Purt B, Shin S, Thavikulwat A, Lu Z, Chew E, Group A, Ajilore P, Akman A, Azar N, Azar W, Chan B, Cox V, Dave A, Dhanjal R, Donovan M, Farrell M, Finkel F, Goblirsch T, Ha W, Hill C, Kumar A, Kent K, Lee A, Patel P, Peprah D, Piliponis E, Selzer E, Swaby B, Tenney S, Zeleny A. DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity. Ophthalmology 2022, 129: 571-584. PMID: 34990643, PMCID: PMC9038670, DOI: 10.1016/j.ophtha.2021.12.017.Peer-Reviewed Original ResearchConceptsAge-related cataractSingapore Malay Eye StudyAnterior segment photographsCortical lens opacitiesPosterior subcapsular cataractCommon typeSlit-lamp photographsLeast common typeMedical studentsEye StudyNuclear sclerosisSubcapsular cataractLens opacitiesCataract typesRetroillumination photographsCataract assessmentOphthalmologistsCataract severityCataractExternal validationDiagnosisSeveritySclerosisStudy dataset