DDLSNet: A Novel Deep Learning-Based System for Grading Funduscopic Images for Glaucomatous Damage.

Rasheed, Haroon Adam; Davis, Tyler; Morales, Esteban; Fei, Zhe; Grassi, Lourdes; De Gainza, Agustina; Nouri-Mahdavi, Kouros; Caprioli, Joseph

Rasheed, Haroon Adam; Davis, Tyler; Morales, Esteban; Fei, Zhe; Grassi, Lourdes; De Gainza, Agustina; Nouri-Mahdavi, Kouros; Caprioli, Joseph.

Afiliación

Rasheed HA; University of California Los Angeles David Geffen School of Medicine, Los Angeles, California.
Davis T; Department of Computer Science, University of California Los Angeles, Los Angeles, California.
Morales E; Glaucoma Division, Jules Stein Eye Institute, Los Angeles, California.
Fei Z; University of California Los Angeles Jonathan and Karin Fielding School of Public Health, Los Angeles, California.
Grassi L; Department of Biostatistics, University of California Los Angeles, Los Angeles, California.
De Gainza A; Glaucoma Division, Jules Stein Eye Institute, Los Angeles, California.
Nouri-Mahdavi K; Glaucoma Division, Jules Stein Eye Institute, Los Angeles, California.
Caprioli J; Glaucoma Division, Jules Stein Eye Institute, Los Angeles, California.

Ophthalmol Sci ; 3(2): 100255, 2023 Jun.

Article en En | MEDLINE | ID: mdl-36619716

Palabras clave

ARW, absent rim width; CI, confidence interval; DDLS; DDLS, disc damage likelihood scale; DiscNet, disc size classification model; Glaucoma; MAE, mean average error; Neural network; ODP, optic disc photograph; RimIoU, rim intersection over union; RimNet, rim segmentation model; Segmentation; mRDR, minimum rim-to-disc ratio

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Ophthalmol Sci Año: 2023 Tipo del documento: Article

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