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Solving data quality issues of fundus images in real-world settings by ophthalmic AI

Liu et al.(1) develop a deep-learning-based flow cytometry-like image quality classifier, DeepFundus, for the automated, high-throughput, and multidimensional classification of fundus image quality. DeepFundus significantly improves the real-world performance of established artificial intelligence d...

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Detalles Bibliográficos
Autores principales: Li, Zhongwen, Chen, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975325/
https://www.ncbi.nlm.nih.gov/pubmed/36812885
http://dx.doi.org/10.1016/j.xcrm.2023.100951
Descripción
Sumario:Liu et al.(1) develop a deep-learning-based flow cytometry-like image quality classifier, DeepFundus, for the automated, high-throughput, and multidimensional classification of fundus image quality. DeepFundus significantly improves the real-world performance of established artificial intelligence diagnostics in detecting multiple retinopathies.