DeepLensNet: 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
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