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A Systematic Review of Artificial Intelligence in Prostate Cancer

Authors Van Booven DJ, Kuchakulla M, Pai R, Frech FS, Ramasahayam R, Reddy P, Parmar M, Ramasamy R, Arora H

Received 22 November 2020

Accepted for publication 11 January 2021

Published 22 January 2021 Volume 2021:13 Pages 31—39

DOI https://doi.org/10.2147/RRU.S268596

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Jan Colli


Derek J Van Booven,1 Manish Kuchakulla,2 Raghav Pai,2 Fabio S Frech,2 Reshna Ramasahayam,2 Pritika Reddy,2 Madhumita Parmar,2 Ranjith Ramasamy,2,3 Himanshu Arora1– 3

1John P Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA; 2Department of Urology, Miller School of Medicine, University of Miami, Miami, FL, USA; 3The Interdisciplinary Stem Cell Institute, Miller School of Medicine, University of Miami, Miami, FL, USA

Correspondence: Himanshu Arora Email Hxa287@miami.edu

Abstract: The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making. Tools like PSA levels, MRI guided biopsies, genomic biomarkers, and Gleason grading are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Nevertheless, diagnosis tracking and subsequent risk stratification often lend itself to significant subjectivity. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.

Keywords: prostate cancer, active surveillance, clinical trials, artificial intelligence

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