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A Systematic Review of Artificial Intelligence in Prostate Cancer
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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7837533/ https://www.ncbi.nlm.nih.gov/pubmed/33520879 http://dx.doi.org/10.2147/RRU.S268596 |
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author | Van Booven, Derek J Kuchakulla, Manish Pai, Raghav Frech, Fabio S Ramasahayam, Reshna Reddy, Pritika Parmar, Madhumita Ramasamy, Ranjith Arora, Himanshu |
author_facet | Van Booven, Derek J Kuchakulla, Manish Pai, Raghav Frech, Fabio S Ramasahayam, Reshna Reddy, Pritika Parmar, Madhumita Ramasamy, Ranjith Arora, Himanshu |
author_sort | Van Booven, Derek J |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7837533 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-78375332021-01-28 A Systematic Review of Artificial Intelligence in Prostate Cancer Van Booven, Derek J Kuchakulla, Manish Pai, Raghav Frech, Fabio S Ramasahayam, Reshna Reddy, Pritika Parmar, Madhumita Ramasamy, Ranjith Arora, Himanshu Res Rep Urol Review 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. Dove 2021-01-22 /pmc/articles/PMC7837533/ /pubmed/33520879 http://dx.doi.org/10.2147/RRU.S268596 Text en © 2021 Van Booven et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Review Van Booven, Derek J Kuchakulla, Manish Pai, Raghav Frech, Fabio S Ramasahayam, Reshna Reddy, Pritika Parmar, Madhumita Ramasamy, Ranjith Arora, Himanshu A Systematic Review of Artificial Intelligence in Prostate Cancer |
title | A Systematic Review of Artificial Intelligence in Prostate Cancer |
title_full | A Systematic Review of Artificial Intelligence in Prostate Cancer |
title_fullStr | A Systematic Review of Artificial Intelligence in Prostate Cancer |
title_full_unstemmed | A Systematic Review of Artificial Intelligence in Prostate Cancer |
title_short | A Systematic Review of Artificial Intelligence in Prostate Cancer |
title_sort | systematic review of artificial intelligence in prostate cancer |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7837533/ https://www.ncbi.nlm.nih.gov/pubmed/33520879 http://dx.doi.org/10.2147/RRU.S268596 |
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