Skip to main content

REVIEW article

Front. Oncol.
Sec. Genitourinary Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1440626
This article is part of the Research Topic The Role of AI in GU Oncology View all articles

Application of artificial intelligence in the diagnosis and treatment of urinary tumors

Provisionally accepted
ying M. Zhu ying M. Zhu 1Guohua Zhao Guohua Zhao 2*
  • 1 Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China
  • 2 Liaoning Cancer Hospital, China Medical University, Shenyang, China

The final, formatted version of the article will be published soon.

    Diagnosis and treatment of urological tumors, relying on auxiliary data such as medical imaging, while incorporating individual patient characteristics into treatment selection, has long been a key challenge in clinical medicine. Traditionally, clinicians used extensive experience for decision-making, but recent artificial intelligence (AI) advancements offer new solutions. Machine learning (ML) and deep learning (DL), notably convolutional neural networks (CNNs) in medical image recognition, enable precise tumor diagnosis and treatment. These technologies analyze complex medical image patterns, improving accuracy and efficiency. AI systems, by learning from vast datasets, reveal hidden features, offering reliable diagnostics and personalized treatment plans. Early detection is crucial for tumors like renal cell carcinoma (RCC), bladder cancer (BC), and Prostate Cancer (PCa). AI, coupled with data analysis, improves early detection and reduces misdiagnosis rates, enhancing treatment precision. AI's application in urological tumors is a research focus, promising a vital role in urological surgery with improved patient outcomes. This paper examines ML, DL in urological tumors, and AI's role in clinical decisions, providing insights for future AI applications in urological surgery.

    Keywords: early diagnosis, Treatment, Urological tumors, artificial intelligence, medical imaging

    Received: 29 May 2024; Accepted: 25 Jul 2024.

    Copyright: © 2024 Zhu and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Guohua Zhao, Liaoning Cancer Hospital, China Medical University, Shenyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.