Naik Nithesh, Tokas Theodoros, Shetty Dasharathraj K, Hameed B M Zeeshan, Shastri Sarthak, Shah Milap J, Ibrahim Sufyan, Rai Bhavan Prasad, Chłosta Piotr, Somani Bhaskar K
Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Krnataka, India.
iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India.
J Clin Med. 2022 Jun 21;11(13):3575. doi: 10.3390/jcm11133575.
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.
本综述旨在介绍深度学习（DL）在前列腺癌诊断和治疗中的应用。由于技术进步，计算机视觉在我们的日常生活中所占比重越来越大。计算能力的这些进步使得在大型数据集上训练更广泛、更复杂的深度学习模型成为可能。泌尿科医生发现这些技术对他们的工作有帮助，并且已经开发了许多这样的模型来辅助前列腺癌的识别、治疗和手术操作。本综述将对这些用于前列腺癌管理的深度学习模型和技术进行系统概述和总结。我们对2000年至2021年过去二十年的英文文章进行了文献检索，这些文章发表在Scopus、MEDLINE、Clinicaltrials.gov、Science Direct、Web of Science和谷歌学术上。初步检索共识别出224篇文章。筛选后，确定了64篇与泌尿外科应用相关的文章，其中24篇被确定为仅与前列腺癌的诊断和治疗相关。深度学习模型的不断改进应推动更多关注深度学习应用的研究。重点应是将模型改进到准备好在临床实践中实施的阶段。未来的研究应优先开发能够在加密图像上进行训练的模型，以增加数据共享和可及性。
