Suspect glaucoma detection from corneal densitometry supported by machine learning

PURPOSE: To discriminate suspect glaucomatous from control eyes using corneal densitometry based on the statistical modeling of the pixel intensity distribution of Scheimpflug images. METHODS: Twenty-four participants (10 suspect glaucomatous and 14 control eyes) were included in this retrospective...

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Detalles Bibliográficos
Autores principales: García-Jiménez, Andrés, Consejo, Alejandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732483/
https://www.ncbi.nlm.nih.gov/pubmed/36210294
http://dx.doi.org/10.1016/j.optom.2022.09.002

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732483/
https://www.ncbi.nlm.nih.gov/pubmed/36210294
http://dx.doi.org/10.1016/j.optom.2022.09.002

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