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

Abstract 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...

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Bibliographic Details
Published in:Insights into Imaging
Main Authors: Nikita Sushentsev, Nadia Moreira Da Silva, Michael Yeung, Tristan Barrett, Evis Sala, Michael Roberts, Leonardo Rundo
Format: Article
Language:English
Published: SpringerOpen 2022-03-01
Subjects:
Prostate cancer
MRI
Artificial intelligence
Deep learning
Machine learning
Online Access:https://doi.org/10.1186/s13244-022-01199-3

Internet

https://doi.org/10.1186/s13244-022-01199-3

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