Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review

OBJECTIVES: We systematically reviewed the current literature evaluating the ability of fully-automated deep learning (DL) and semi-automated traditional machine learning (TML) MRI-based artificial intelligence (AI) methods to differentiate clinically significant prostate cancer (csPCa) from indolen...

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Detalles Bibliográficos
Autores principales: Sushentsev, Nikita, Moreira Da Silva, Nadia, Yeung, Michael, Barrett, Tristan, Sala, Evis, Roberts, Michael, Rundo, Leonardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2022
Materias:
Critical Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960511/
https://www.ncbi.nlm.nih.gov/pubmed/35347462
http://dx.doi.org/10.1186/s13244-022-01199-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960511/
https://www.ncbi.nlm.nih.gov/pubmed/35347462
http://dx.doi.org/10.1186/s13244-022-01199-3

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