Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review
This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more c...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/13/3575 |
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| author | Nithesh Naik Theodoros Tokas Dasharathraj K. Shetty B.M. Zeeshan Hameed Sarthak Shastri Milap J. Shah Sufyan Ibrahim Bhavan Prasad Rai Piotr Chłosta Bhaskar K. Somani |
| author_facet | Nithesh Naik Theodoros Tokas Dasharathraj K. Shetty B.M. Zeeshan Hameed Sarthak Shastri Milap J. Shah Sufyan Ibrahim Bhavan Prasad Rai Piotr Chłosta Bhaskar K. Somani |
| author_sort | Nithesh Naik |
| collection | DOAJ |
| description | This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more complex DL models to be trained on large datasets. Urologists have found these technologies help them in their work, and many such models have been developed to aid in the identification, treatment and surgical practices in prostate cancer. This review will present a systematic outline and summary of these deep learning models and technologies used for prostate cancer management. A literature search was carried out for English language articles over the last two decades from 2000–2021, and present in Scopus, MEDLINE, Clinicaltrials.gov, Science Direct, Web of Science and Google Scholar. A total of 224 articles were identified on the initial search. After screening, 64 articles were identified as related to applications in urology, from which 24 articles were identified to be solely related to the diagnosis and treatment of prostate cancer. The constant improvement in DL models should drive more research focusing on deep learning applications. The focus should be on improving models to the stage where they are ready to be implemented in clinical practice. Future research should prioritize developing models that can train on encrypted images, allowing increased data sharing and accessibility. |
| first_indexed | 2024-03-09T04:07:02Z |
| format | Article |
| id | doaj.art-dee21ea1377843a58a968a9a4bd06c8c |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T04:07:02Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-dee21ea1377843a58a968a9a4bd06c8c2023-12-03T14:06:06ZengMDPI AGJournal of Clinical Medicine2077-03832022-06-011113357510.3390/jcm11133575Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature ReviewNithesh Naik0Theodoros Tokas1Dasharathraj K. Shetty2B.M. Zeeshan Hameed3Sarthak Shastri4Milap J. Shah5Sufyan Ibrahim6Bhavan Prasad Rai7Piotr Chłosta8Bhaskar K. Somani9Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Krnataka, IndiaDepartment of Urology and Andrology, General Hospital Hall i.T., Milser Str. 10, 6060 Hall in Tirol, AustriaDepartment of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaiTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaiTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, IndiaiTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, IndiaiTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, IndiaDepartment of Urology, Jagiellonian University in Krakow, Gołębia 24, 31-007 Kraków, PolandiTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, IndiaThis review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more complex DL models to be trained on large datasets. Urologists have found these technologies help them in their work, and many such models have been developed to aid in the identification, treatment and surgical practices in prostate cancer. This review will present a systematic outline and summary of these deep learning models and technologies used for prostate cancer management. A literature search was carried out for English language articles over the last two decades from 2000–2021, and present in Scopus, MEDLINE, Clinicaltrials.gov, Science Direct, Web of Science and Google Scholar. A total of 224 articles were identified on the initial search. After screening, 64 articles were identified as related to applications in urology, from which 24 articles were identified to be solely related to the diagnosis and treatment of prostate cancer. The constant improvement in DL models should drive more research focusing on deep learning applications. The focus should be on improving models to the stage where they are ready to be implemented in clinical practice. Future research should prioritize developing models that can train on encrypted images, allowing increased data sharing and accessibility.https://www.mdpi.com/2077-0383/11/13/3575artificial intelligencedeep learningconvolutional neural networkcomputer-aided detectionmedical imagingGleason grading |
| spellingShingle | Nithesh Naik Theodoros Tokas Dasharathraj K. Shetty B.M. Zeeshan Hameed Sarthak Shastri Milap J. Shah Sufyan Ibrahim Bhavan Prasad Rai Piotr Chłosta Bhaskar K. Somani Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review Journal of Clinical Medicine artificial intelligence deep learning convolutional neural network computer-aided detection medical imaging Gleason grading |
| title | Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review |
| title_full | Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review |
| title_fullStr | Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review |
| title_full_unstemmed | Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review |
| title_short | Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review |
| title_sort | role of deep learning in prostate cancer management past present and future based on a comprehensive literature review |
| topic | artificial intelligence deep learning convolutional neural network computer-aided detection medical imaging Gleason grading |
| url | https://www.mdpi.com/2077-0383/11/13/3575 |
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