Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review

PURPOSE: This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability. METHODS: MEDLI...

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Detalles Bibliográficos
Autores principales: Bulfamante, Antonio Mario, Ferella, Francesco, Miller, Austin Michael, Rosso, Cecilia, Pipolo, Carlotta, Fuccillo, Emanuela, Felisati, Giovanni, Saibene, Alberto Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Review Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849161/
https://www.ncbi.nlm.nih.gov/pubmed/36260141
http://dx.doi.org/10.1007/s00405-022-07701-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849161/
https://www.ncbi.nlm.nih.gov/pubmed/36260141
http://dx.doi.org/10.1007/s00405-022-07701-3

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