Identification of Women with High Grade Histopathology Results after Conisation by Artificial Neural Networks

BACKGROUND: The aim of the study was to evaluate if artificial neural networks can predict high-grade histopathology results after conisation from risk factors and their combinations in patients undergoing conisation because of pathological changes on uterine cervix. PATIENTS AND METHODS: We analyse...

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Detalles Bibliográficos
Autores principales: Mlinaric, Marko, Krizmaric, Miljenko, Takac, Iztok, Repse Fokter, Alenka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Sciendo 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400436/
https://www.ncbi.nlm.nih.gov/pubmed/35776841
http://dx.doi.org/10.2478/raon-2022-0023

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400436/
https://www.ncbi.nlm.nih.gov/pubmed/35776841
http://dx.doi.org/10.2478/raon-2022-0023

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