Diagnostic accuracy of code-free deep learning for detection and evaluation of posterior capsule opacification

OBJECTIVE: To train and validate a code-free deep learning system (CFDLS) on classifying high-resolution digital retroillumination images of posterior capsule opacification (PCO) and to discriminate between clinically significant and non-significant PCOs. METHODS AND ANALYSIS: For this retrospective...

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Detalles Bibliográficos
Autores principales: Huemer, Josef, Kronschläger, Martin, Ruiss, Manuel, Sim, Dawn, Keane, Pearse A, Findl, Oliver, Wagner, Siegfried K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Lens
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9174773/
https://www.ncbi.nlm.nih.gov/pubmed/36161827
http://dx.doi.org/10.1136/bmjophth-2022-000992

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9174773/
https://www.ncbi.nlm.nih.gov/pubmed/36161827
http://dx.doi.org/10.1136/bmjophth-2022-000992

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